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Aug 12 2019

Six things we’ve learned about power

And their implications for evaluation

Power is an integral aspect of social change initiatives. It can shape relationships and enable or constrain the agency and impact of social change actors. Change actors constantly contend with power as they work to advance systems and structural change. But despite its significance, power is often missing from conversations about funding and evaluating social change.

In the past year at Innovation Network, we’ve noticed an increase in the number of nonprofits and funders asking us to help them assess and learn about the power-building efforts of organizations, networks, and movements.

To that end, we’re reviewing academic and practice-based works to better understand power, how it shows up in change ecosystems, and how we can appropriately evaluate it. Here’s what we’ve learned so far.

Photo by Miguel Bruna on Unsplash

Lesson 1: Concepts of power are contested.

A constant across the literature is the lack of a common definition of power. Various pieces we’ve read describe power in turn as “sociologically amorphous,” “perpetually contested,” “polymorphous,” with a virtual “confetti of definitions and theories.”

So this wouldn’t be a real piece about power without first giving the usual disclaimer that definitions of power are contested, overwrought, and evolving.

If you’ve ever had a conversation with anyone about power, you’ve likely experienced this. I’ve personally found myself in discussions with people about power where it becomes immediately apparent that we’re talking past each other. While some people want to talk about structural types of power, others will talk about power building, and others still about power dynamics.

Implications for evaluation: If we want to assess power and power building, its critical to understand that definitions of power are numerous, and people will likely show up to the evaluation process with different understandings of power.

So how do we prioritize the varying definitions and concepts? We believe centering community and social change actors’ definitions and understandings of power will be critical. Community organizers, advocates, and movement builders have grappled with power and power building long before it became buzzy in the philanthropic and evaluation sectors. There is a lot we can learn from social change actors about power.

Lesson 2: Power is dynamic and multidimensional.

The lack of shared definition of power underscores that power is dynamic and multidimensional.

Academics and practitioners have identified countless ways that power manifests in the world. John Gaventa, an influential theorist of power, created the powercube in an effort to identify the different levels, spaces, and forms of power:

  • Levels: Refers to the different levels where power is exercised, from the individual, family, organization levels up to the local, state, national, and global levels.
  • Spaces: Refers to the spaces where power is exercised for decision-making and action. Gaventa describes spaces as being closed, invited, or claimed.
  • Forms: Refers to the different ways power shows up, including its visible, hidden, or invisible forms (more on that in Lesson 3).

Others have built on these levels, spaces, and forms to identify different expressions of power (Lesson 5) and different types of power (Lesson 6).

The levels, spaces, forms, and expressions of power are not static. These dimensions of power are interrelated and dynamic, shifting and evolving over time. Gaventa described it this way: “Each dimension of the powercube is constantly interrelating with the other, constantly changing the synergies of power. For instance, what happens at global decision-making levels can affect the spaces available for participation and engagement; which spaces are available affect the forms of power within them.”

Implications for evaluation: All systems change efforts exist in complex ecosystems of power. These ecosystems include a diverse range of social change actors, their opposition, and the decisionmakers, institutions, and systems they seek to influence. Power shapes the relationships between these actors and their ability to advance their interests. Power can take many forms and show up in countless different ways.

To address this complexity, we believe evaluating power and power building will require a developmental approach. Evaluators of power will need to be ready for shifting power dynamics and to identify and assess new forms and sources of power as they emerge.

Lesson 3: Examining power can help us understand the structural forces that enable or constrain change efforts.

Power over is the longest-standing, most recognized expression of power. This concept sees power as being fundamentally repressive and negative, all about domination and control. Some of the earliest theorists described power this way. In recent years, some theorists have started taking a more nuanced view of power over. They recognize that while power can be negative and repressive, dominant social structures and historical, social, and cultural forces can exert power in a way that is beneficial to social change efforts.

Either way, power over underscores that dominant historical and structural forces can have a powerful influence on the relationships, capacity, and impact of change efforts.

In 1974, Stephen Lukes provided one of the most oft-cited theories of power over in his book, Power: A Radical View. In this book, Lukes introduced the “three faces” of power over — visible, hidden, and invisible.

  • Visible: Visible power refers to power that is publicly visible or that takes place in formal decisionmaking spaces. Arenas for visible power include laws, legislatures, and courts.
  • Hidden: Hidden power is exercised by powerholders to maintain their power and privilege by systematically excluding people and their interests from decisionmaking tables and processes. Hidden power can manifest in how issues are framed and how institutions are structured.
  • Invisible: Invisible power shapes people’s worldviews, ideologies, and sense of agency. Powerholders may exercise invisible power by developing dominant narratives and tailoring the information available to the public to hide the existence and sources of injustice.

Lukes’s three faces of power have gotten a lot of play in social change spaces and have shown up in the work of other organizations, including Just Associates and the Grassroots Policy Project. These organizations have used the three faces of power to encourage social change organizations to take a more holistic view of the forces that facilitate or inhibit social change.

Implications for evaluation: By examining and mapping the structural forces that exercise power over a change ecosystem, we can better understand the contextual factors, systems, and structures that enable or constrain social change. This understanding makes evaluation findings more valid and useful, by deepening our understanding of which power-building approaches work under what conditions.

Lesson 4: Power building is at the heart of grassroots-led change efforts and can help us understand the progress and success of change initiatives.

Power doesn’t just lie in social structures and institutions, social change actors also have the ability to build and exercise their own power.

Gene Sharp, one of the world’s leading thinkers on nonviolent action, was a big believer in the social view of power. This idea emphasizes that the power of decisionmakers and institutions depends on the consent and cooperation of ordinary people. People can wield power by collectively giving or withdrawing their support for systems, institutions, and decisionmakers.

The idea that power lies in the hands of the public is one that is essential to grassroots-led change efforts like community organizing and social movements. Grassroots efforts flip the script on who is the powerholder, stressing that power lies not just in formal institutions and visible decisionmakers, but also in the general public.

For that reason, power building is often fundamental to the theory of change of grassroots-led change efforts. Building long-term power is often not just a means to an end but a critical end in itself.

Implications for evaluation: Power building is an important lens through which we can view the progress and success of change efforts. Assessing power building will require us to think differently about how success is defined. Contrary to what some might think, policy and systems change is not the end goal of all social change. “Winning” is also about building and maintaining long-term power in communities. How would it change our understanding and assessment of social change efforts if we saw them through a lens of power building?

Lesson 5: Power to, power with, and power within are three expressions of power building that provide us with a starter framework for assessing power.

In A New Weave of Power, People, and Politics, Lisa VeneKlasen and Valerie Miller made the case that power can be used as a positive force to advance affirmative change in society. They outlined three core expressions of power building: power to, power with, and power within.

  • Power to (power as capacity): Most of the time when people talk about power building, power to is what they’re talking about. Power to refers to your ability to exercise agency or, as Martin Luther King Jr. said, “Power, properly understood, is nothing but the ability to achieve purpose. It is the strength required to bring about social, political, and economic change.” Having power to means you have the capacity, resources, and opportunities to advance your interests.
  • Power with (power as relationships): Power with emphasizes that relationships are a significant source of power. Power with is about building collective strength across varying interests and stakeholders. Because long-term change often requires collective action, power with is a critical form of power building for social change initiatives.
  • Power within (power as individual agency): Power within is about developing a person’s individual agency, self-empowerment, and self-worth. Some social change efforts, such as community organizing, seek to build power within the individual community members and leaders they serve and organize. In transformative organizing practice, for instance, social transformation is seen as being intertwined with personal transformation; social transformation is only possible when individuals have a sense of self-worth and individual agency.

Implications for evaluation: The three expressions of power building have helped us better understand the forms of power social change actors can build. We believe they provide us with a starter framework for identifying and assessing power building in social change efforts that can be adapted to the specific interventions we are called to evaluate.

Lesson 6: Governing, people, and narrative power are three common types of power building that advance structural change.

Some social change organizations and practitioners have started to identify, define, and operationalize the types of power building that are needed to advance change. Three power-building concepts have emerged that seem to be common across different organizations and change efforts: governing/governance power, people power, and narrative power.

Governing Power/Independent Political Power: Although they have different names, governing power and independent political power (IPP) have similar definitions and elements. Both are about building power to govern based on the values and priorities of grassroots communities. Governing power is about building political infrastructure and capacity that is independent from the dominant political parties. It goes beyond policy and electoral change to advance structural change and influence narratives, norms, and values. Some select resources about governing power/IPP:

  • Demos. Independent Political Power.
  • Grassroots Policy Project. Organizing for Governing Power.
  • University of Southern California Program for Environmental and Regional Equity. Power and possibilities: A changing states approach to Arizona, Georgia, and Minnesota.

People Power: Similar to the social view of power discussed above, people power emphasizes that power comes from the engagement of ordinary people. People power is often about building power with an active, grassroots base. However, in the context of social movements especially, people power is also about building power to build, mobilize, and sustain large-scale public support. Select resources:

  • Ayni Institute. Winning with the Masses.
  • University of Southern California Program for Environmental and Regional Equity. Sustaining People Power.

Narrative Power: Narrative power is fundamentally about the power to transform and hold dominant public narratives and ideologies and to limit the influence of opposing narratives. Rashad Robinson from Color of Change sums it up nicely: “Narrative power is not merely the presence of our issues or issue frames on the front page. Rather, it is our ability to make that presence powerful — to be able to achieve presence in a way that forces change in decision-making and in the status quo.” Select resources:

  • Grassroots Policy Project. Worldview and the contest of ideas.
  • Rashad Robinson. Changing our narrative about narrative: the infrastructure required for building narrative power.

Implications for evaluation: While change initiatives do build other types of power, governing, people, and narrative power are often present in community organizing, electoral organizing, advocacy, and social movements. Given the contested nature of power concepts, we are finding that having some definition about these types of power building (and having a set of resources to draw on) provide a helpful starting place for discussions about power with funders, evaluators, and social change actors.

As more funders, organizers, and advocates start paying attention to power, it will become an increasingly important competency for evaluators who assess structural change efforts to know how power shows up in change ecosystems, how it is built, and how to appropriately measure. Over the next year, we’re building on these learnings to develop a set of resources for evaluating power building. Stay tuned!

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Six things we’ve learned about power was originally published in InnovationNetwork on Medium, where people are continuing the conversation by highlighting and responding to this story.

Written by cplysy · Categorized: innovationnet

Aug 05 2019

Evaluator Competencies Series: Transparency

1.6 Is committed to transparency in all aspects of the evaluation.

transparent adjective
trans·​par·​ent 

a: free from pretense or deceit : FRANK

b: easily detected or seen through : OBVIOUS

c: readily understood

d: characterized by visibility or accessibility of information especially concerning business practices

Merriam-Webster Dictionary

To reflect on this competency, I decided to see what the definition of “transparency” is. I usually think of transparency in the sense of “sharing all the information”, which is a bit more extreme than one can actually be in an evaluation. For example, we have an ethical responsibility to maintain confidentiality for participants in our evaluations when they want their identity to be kept confidential. Sometimes we are working with proprietary information that the organization requires to be kept confidential. So as with so many things, being “transparent” requires a bit of nuanced thinking.

Glasswinged Butterfly

I used to work with someone who talked about her role in a communication chain in a hierarchical organization, where information came from the top and was cascaded down through the org chart. Sometimes, information was only allowed to be shared to a certain level – say, it could go from the VPs to the EDs to the Directors, but the Directors were not allowed to share it with the Managers – at least not yet. And this person’s (who was in a Director role) approach to it was to tell their managers “I do know this information but I am not allowed to share it with you at this time.” And then they would give the reason (e.g., “Leadership is planning to do X, but until it is signed off by the board of directors, it’s not official and so they do not want put this information out broadly in case the board does not sign off it on, as it could cause confusion.”). And then they would make a commitment to tell their managers as soon as they were allowed to. This approach stuck with me, because it was honest (they weren’t saying “I don’t know this information” when they really did know, which is an approach I’d seen others take in these types of situations) and it was as informative as it was possible to be given the situation – giving a reason why they weren’t allowed to share the information at that time, rather than just saying “I’m not allowed to say”. I find that not giving a reason usually results in people coming up with their own theories about what information is being kept hidden – and that ends up causing rumours and confusion. So I think that this (sharing what you can and being honest about what you can’t share and why) can be a useful approach to being transparent. Of course, there can be good reasons or bad reasons for not wanting to share information, so I think we also have a responsibility to think critically about the reasons why an organization might not want to share and to push back in situations where appropriate (e.g., if an organization wants to suppress evaluation findings because they think it makes them look bad, as I talked about last week, I’d push back on that).


I was interested to see that the definition of “transparent” isn’t just about making information accessible, but also making information “readily understood” and “free from pretence or deceit”. These are things I can get behind. Obviously, a credible evaluation should not include anything deceitful, but I think making information “readily understood” is something that is sometimes overlooked. There are so many ways that we can exclude people from evaluation by not being “readily understood” – whether that be by the way we design our evaluations, the ways we recruit participants, the methods we use, or the way the report the findings. There seems to be a lot of interest in the evaluation world around data visualization – i.e., presenting data in ways that actually convey the meaning of them. This is something that my team and I are actively working to get better at. And there’s interest in alternative reporting formats – i.e., not just handing over a 200 page report, but actually thinking about ways to report evaluation findings that work for those who are interested in those findings.

Glasswinged butterfly (Greta oto)

Something I see spoken about less often, but that I think about a lot, is the use of language. I’m a bit fan of clear, simple language when writing 1Though I will admit that I have a tendency to be wordy, I write complicated sentences, and I overuse footnotes to an excessive degree. because I think that if I’m writing something, I want people to understand it. I mean, isn’t that why I’m writing it? I try to avoid jargon (or at the very least explain any jargon that I use) and prefer to pick a simple word over an obscure one. But I often see writing that is full of jargon, and unnecessarily large and obscure words. Part of me thinks that people write this way in an attempt to look intelligent. And I have seen situations where people use jargon as a way to try to cover up that they don’t know what they are talking about (which becomes evident as soon as you start asking questions like “What do you mean when you use the word X?”) An even more cynical part of me thinks that people write like this in order to exclude other people, by making the knowledge they are ostensibly trying to “share” non-understandable by “others” who don’t have the same training/background as them. After all, knowledge is power and keeping knowledge away from others by making it not understandable to others is a way of holding onto power. Which to me, is another reason to make the effort to make my writing as clear and easy to understand as possible.

At any rate, I hadn’t really thought about making information “readily accessible” as being part of “transparency” before, but it makes sense when I think about it.

Image Sources:

  • Orange glass winged butterfly posted on Flickr by Alias 0591 with a Creative Commons license.
  • Black glass winged butterfly posted on Flickr by Mary Shattock with a Creative Commons license.

Footnotes   [ + ]

1. ↑ Though I will admit that I have a tendency to be wordy, I write complicated sentences, and I overuse footnotes to an excessive degree.

Written by cplysy · Categorized: drbethsnow

Aug 04 2019

we don’t need another p < 0.05

Photo by  Jordan McDonald  on  Unsplash . I swear the picture is relevant.

Photo by Jordan McDonald on Unsplash. I swear the picture is relevant.


If you listened to our recent episode of Eval Cafe with Michael Quinn Patton on principles-focused evaluation, you’ll remember him sharing his new favourite example of principles in action. It’s from the introductory article of a recent special issue of The American Statistician, which is all about moving beyond the use of p < 0.05 as the threshold for determining statistical significance. The article offers an impassioned explanation of why abandoning the entire concept of statistical significance is necessary and also outlines the beginnings of an alternative practice for valuing and interpreting statistical findings. The reason it showed up in the podcast is because the authors ground this new framework in principles, or flexible advice that can guide decisions and give direction, but must be adapted and interpreted in context. In comparison, p < 0.05 is a rule—it is applied the same way regardless of any contextual factors. (Check out the podcast and also Michael’s book, Principles-Focused Evaluation, to learn more about the implications of principles for evaluative work.) Specifically, the principles that the authors offer are, “Accept uncertainty. Be thoughtful, open, and modest” (or “ATOM”, as a mnemonic), and the remainder of the issue (43 articles worth!) goes on to offer more depth around the issues of p < 0.05 and the discussion of alternatives.

For an academic publication about statistics, it is, frankly, stirring. Read this:

“At times in this editorial and the papers you’ll hear deep dissonance, the echoes of ‘statistics wars’ still simmering today (Mayo 2018). At other times you’ll hear melodies wrapping in a rich counterpoint that may herald an increasingly harmonious new era of statistics. To us, these are all the sounds of statistical inference in the 21st century, the sounds of a world learning to venture beyond ‘p < 0.05.’ This is a world where researchers are free to treat ‘p = 0.051’ and ‘p = 0.049’ as not being categorically different, where authors no longer find themselves constrained to selectively publish their results based on a single magic number. … As we venture down this path, we will begin to see fewer false alarms, fewer overlooked discoveries, and the development of more customized statistical strategies. Researchers will be free to communicate all their findings in all their glorious uncertainty, knowing their work is to be judged by the quality and effective communication of their science, and not by their p-values.”

(Has a paper on inferential statistical testing ever brought tears to your eyes? This brought tears to mine. The freedom in it, the vision of it, the clarion call to a remembered sacred purpose of meaningful scientific discovery—it’s poetry. And, honestly, the whole article is a delight to read and it’s open access. Treat yourself!)

So what’s wrong with p < 0.05? Why devote an entire issue of an journal to explaining why it should be done away with?

The problem is that it’s a seductively simple idea that was never equipped to be used the way that it has been. We took something that might have been an okay guideline for thinking about whether to explore a statistical relationship further and turned it into something so hard-and-fast that careers are made and broken on it, that people are incentivized to do bad science because of it (whether that’s bad actors manipulating results or more subtly the cumulative, unintentional harm of something like the “file drawer problem”). We took p < 0.05 and applied it thoughtlessly, rigidly, imperiously, and with total disregard for context. To the point that the statisticians tell us that the solution to p < 0.05 is not to tweak it, to start using “p < 0.10” instead or confidence intervals or to come up with a more complicated system of rules that let us keep doing essentially the same thing as we always have but “better” this time. Instead they ask us to recognize that the entire concept of a fixed threshold for statistical inference is flawed and we must shift to a way of thinking that incentivizes nuance, humility, and care. It is a call to transformation, to a world beyond p < 0.05.

So why am I bringing this up, since this post isn’t actually about statistics? (Surprise!) Bear with me—we’re about to go on a bit of journey.

I bring up p < 0.05 and its critiques because I realized that to me it speaks to the same issues I have with sex and gender binaries*. We have taken what is a fluid, complex interplay based around complementary elements that are meant to be more like tent poles, lifting up a fabric of possibility that drapes around and between them, and we have stripped them of nuance and severed what connects them. We have sacrificed depth and breadth in our understanding and experience of sex and gender for convenience, control, and predictability, leaving ourselves with two bare stakes in the ground. Because just like with p < 0.05, there’s something seductive about the notion of a fundamental, highly predictive, nearly-universal binary division of sex and gender, something deeply appealing about the idea that this dichotomy is rooted in biology and manifested at all individual and sociocultural levels. We can see the evidence of the attraction in how often it shapes the most basic of our everyday activities—going to the washroom, putting on clothes, talking about other people, filling out forms, etc. All of which feeds back into the perception that these are innate, meaningful, nigh-universal differences, which is why it is so powerful to take note of where the contrasts, contradictions, and variations arise, in language, biology, psychology, culture, and elsewhere. And how these variations are not statistical noise and or mere outliers but vital parts of the whole picture.

Because I’m not saying there is no pattern to sex and gender. I’m saying that the pattern has been reduced, over-simplified, and blown completely out of proportion, with individual components being mistaken for the entire phenomenon, like a painting being the sum of the pots of paints used to produce it. Now imagine a world in which every piece of art was classified based on its relative proportion of red and blue hues just because those are two primary colours. Whole galleries divided into “red wings” and “blue wings”. Vast swathes of art from across the colour spectrum lumped together without regard to the rest of their palettes (and never mind all the other defining characteristics one could consider about them). New technology devised for the sole purpose of pinpointing with stunning accuracy the exact amount of red and blue present in a given piece. The stubbornly unclassifiable relegated to storage because it’s only a small proportion anyway and it just upsets what is otherwise such an elegant, simple binary scheme. Because establishing clear, bright lines around sex and gender categories does make life easier in many ways. But convenience comes with a cost (think about Amazon and Uber), usually a profound human cost that is disproportionately distributed along lines of power and influence although we each pay our own price for it.

Rachel Pollack, a science fiction and comic book author and trans woman, captures the heart of the struggle with in her recent essay, “Trans Central Station”, where she shares her experience of coming out as a trans woman in the 1970s (a time before the language of “transition”, “transgender”, and “trans woman” even existed in English):

“What I felt, what I desired was unspeakable because, for me, at least, the words did not exist. Or rather, the telling did not exist. … The mind could not form the thought. I did not wish to tell people and didn’t dare. I simply could not imagine doing it. … I was not trapped in the wrong body, I was trapped in the wrong universe. In order to become who I was, I had to break the world open. I had to embrace a kind of science fiction life. … The physical world may be made out of elementary particles (and dark matter) but the world of our lives is made out of language. With the wrong language, one of strict categories and confinement, the world becomes a fake, a stage set whose actors don’t know they are in a play … Most people do not notice this because their own sense of self, of language, more or less fits the received version of existence. They still suffer, for in a world of strict and very limited categories, they must constantly check themselves against the model of a ‘real man’ or a ‘real woman’. The ones who reveal the fake are the ones who simply cannot make themselves fit. To not fit can bring great pain and often very real danger, yet who else can discover the light behind the screen?”

As I shared when I wrote about my pronouns, the words to describe my gender (and my sex and the relationship between them and my relationship with them) don’t exist in my language. I borrow words like “queer” and “trans” and “nonbinary”, because they come as close as they can, but they are a finger pointing at the moon, not the moon itself. I suppose all of language is a finger pointing at the moon, but there are varying degrees of remembering and forgetting that the finger is not the moon. With p < 0.05, we forgot the moon existed at all, and I think we do the same with “male” and “female”, “man” and “woman”, the lonely tent poles that they are. Because I don’t think my gender is any more mysterious or complex than anyone else’s or any less coherent, save for the misfortune of being born in the wrong language, one that has the idea that these things are governed by rules, clear and delineated and precise. Sex and gender are messy, multifaceted, ill-defined, and exhilarating. They’re never going to boil down to simple rules so we should start thinking about what kinds of principles might help us here. We need to embrace the ambiguity rather than keep trying to erase it, because it belongs to all of us, not just those of us who simply cannot make ourselves fit.

I don’t have 43 articles to share of deep exploration into this issue and possible alternatives. But even if I did, our friends at The American Statistician still had to warn their readers, “What you will NOT find in this issue is one solution that majestically replaces the outsized role that statistical significance has come to play. The statistical community has not yet converged on a simple paradigm for the use of statistical inference in scientific research—and in fact it may never do so. A one-size-fits-all approach to statistical inference is an inappropriate expectation, even after the dust settles from our current remodeling of statistical practice (Tong 2019).” A one-(or-two)-size-fits-all approach to sex and gender is unlikely to be forthcoming either. I also don’t have principles to offer for a better understanding of sex and gender right now, though, “Accept uncertainty. Be thoughtful, open, and modest”, are pretty good ones to start with.

We’re going to keep wrestling with these issues, standing in the world of now while we look to a world beyond. (And that world is coming—you can watch it happen in the flux and growth of the language and the ideas that are becoming available to us.) One thing we need to do while we experience this uncertainty is to not try to negate it or reduce it or look for a newer, better p < 0.05 that will let us carry on as usual. It’s not about tacking on an extra category or two. It’s about letting go of the seductive simplicity altogether, and finding a way forward that allows for nuance and wholeness.

That’s what I find so inspiring about the p < 0.05 article. It’s a beautiful exploration of how (and why) to move from disastrous over-simplification to an adaptive embrace of complexity (and within an institution that is itself complex, with a life and momentum of its own that resists change out of an impulse for self-preservation which is understandable even as we recognize that to resist change is also an act of self-destruction through obsolescence**). It may contain no absolute answers (that would be too simple), but it has an abundance of hope, compassion, and courage, as well as a frank reckoning of the systemic, institutional challenges to such a profound shift. I want to see the same combination of depth, hope, and strategy brought to our conversations around sex and gender as well.

The transformation is out there. What starts as science fiction can become the art that life imitates.

Happy Pride, y’all.


*I’m referring to “sex and gender” throughout because while they can be thought of as different things, they are BOTH complex and BOTH socially-constructed. It’s not a case of “sex is simple, gender is complex”. If you want to know more on that, check out the readings I reference at the end of my pronoun post.

**I think I’m going to start summarizing this paradox as “change and/or die”.


BONUS:

Podcast episode recommendation! Check out the Indigiqueer episode of the All My Relations podcast for a look into gender and sexuality from Indigenous viewpoints.


Photo by  Steve Johnson  on  Unsplash

Photo by Steve Johnson on Unsplash

Written by cplysy · Categorized: carolyncamman

Aug 01 2019

transformation is awkward

Photo by  Francisco J. Villena  on  Unsplash

Photo by Francisco J. Villena on Unsplash


Transformation is awkward.

I mean, we all know that change is hard, but it’s also awkward.

It’s been a big year for me. The last ten months in particular have included a lot of things I never saw coming and just got to run with as they came my way. I’ve made a lot of new friends. I’ve gotten to have some incredibly transformative learning experiences (the Evaluation for Social Change and Transformational Learning program through SFU and the annual Art of Hosting training on Bowen Island being two I can strongly recommend to others!). I’ve been graced with some amazing opportunities to step in and push myself to new levels. I’ve had to deal with some setbacks, disappointments, and missteps, though I contend that all my best mistakes are still ahead of me. (I can only aspire to be a future contributor to a follow-up volume to the wonderfully honest and generously insightful, Evaluation Failures: 22 Tales of Mistakes and Lessons Learned.)

When I think back to myself a year ago, I’m astounded. It’s not that I was doing so terribly before, but it’s such an unexpected, qualitative difference. In less than a year, I’ve transformed my practice. I’ve clarified (at least for now) my guiding principles. I’ve discovered personality traits I didn’t know I had. I’ve changed things I thought were immutable personality traits! (This is why it makes so much more sense to think of personality as a system, as an aside.) My social and professional networks (around here those are basically the same thing) quadrupled at least (I’d give my right arm for a time-lapse network analysis of this, truly). I grew out my dang hair even, which is a big deal! I had that buzzcut longer than I’d lived most places. And, weirdly, I’ve felt more like myself than I can recall for a very long time.

But it’s been uncomfortable. It’s been hard. The biggest, most joyful and breath-taking breakthroughs also brought on periods of grief and despair—grief over the loss of a perceived self, grief over feelings of lost time spent travelling down paths that led away from the core self, despair over how long it takes to find a way back again. I spent a lot of time pondering this visualization of artist block and reminding myself that I am improving all the time, it just doesn’t feel like it when I’m also increasing my capacity to see what else I could be doing.

And even now that I’m acclimating to the emotional waves of change and not getting knocked about quite so much, it’s still awkward. Ungainly. I don’t get to be coolly possessed and confident. My intuition will run ahead of my conscious understanding so I’ll end up feeling strongly about something without being able to articulate why, and then having to backtrack later and explain once I’ve figured it out. Or I’ll find myself entangled in the extinction burst of a past habit and at cross-purposes with myself. A moment of insight and clarity will take the impossible knot I’ve been wrestling with and dissolve it into irrelevance, and I’ll find myself wondering how I managed to waste so much time on something that was resolved so easily. I spend a lot of time feeling incompetent, then realizing that I’m way more competent than I think, and then feeling incompetent for thinking I’m incompetent. Argh! I feel inconsistent and inconstancy still reads as a character flaw or moral failing to me. (Another habit yet to be unpicked and re-stitched! Coherency is preferable to constancy, if nothing else.)

Change is just like that, whether it’s coming in drips, tides, or tsunamis. You don’t get to sail into it, smooth and suave, confident that you know exactly what you’re doing and how to do it. Because you don’t! That’s the point. If we knew what we were doing, it wouldn’t be change. But not knowing exactly what you’re doing but kind of maybe knowing what you’re supposed to be doing and possibly doing some of it wrong but going ahead doing it anyway and trying to pull it off with some level of grace and integrity and humility is kind of where it’s at. So thank goodness for having a really high tolerance for embarrassment (not a skill I’d planned to lean on so much in adulthood, oh well) and a good circle of folks to commiserate and celebrate with, awkwardness and all.

(This is an evaluation blog, or at least the blog of an evaluator on their professional evaluation website, so I always feel compelled to draw some kind of evaluation-specific connections or insights or lessons out of what I write about, but I feel like “transformation is awkward” speaks for itself, y’know? Let’s all go a little easier on ourselves.)

Written by cplysy · Categorized: carolyncamman

Jul 30 2019

Complexity eStudy Notes – Session #3

My notes from the third and final part of the American Evaluation Association (AEA) eStudy course being facilitated by Jonny Morell.

  • Jonny answered my question from last week:what is the difference between conceptual use and metaphorical use
    • there are fuzzy boundaries
    • if you think about chaotic systems – “strange attractors” (a.k.a., chaotic attractors)
    • you can do the math to plot a fractal – that is a technical meaning of the word
    • a conceptual meaning of the word – I know it’s not random, but I can’t tell you from one instance to the next where it will be, but I can tell you where it won’t be. You aren’t using the mathematics, but you are using the concept. Conceptual is still grounded in the technical.
    • metaphorical use – a step further away – we have this concept of chaos – that means its unpredictable. Conceptual means you have to “stay close to the mathematical, without doing the math”.
  • he thinks that if you take complex behavoiur seriously, you’ll do better program design and evaluation
  • but not trying to convert everyone to complexity thinking for everything all the time

Unintended Consequences

Complexity
  • he tends to think that unintended consequences are usually negative – any change perturbs a system, and even if some parts of a system aren’t working, it will mess up a system; it’s harder to make things work than to make things not work, so if you perturb a system, it’s more likely that bad things will come out of it
    • he’s heard this from many people with “broad and deep experience” whose work he respects
    • “Programs like to optimize highly correlated outcomes within a system. This is likely to result in problems as other parts of the system adapt to change.
    • Change perturbs systems. Functioning systems require many parts of “fit”, but only a few to cause dysfunction”
  • he recently read about some work that shows this might not be true! But he wants to read more about it.
  • there are always unintended consequences – and if they are good or bad is an important question!
  • examples of unintended consequences provided by an audience member. A medical school started at a northern university to promote more physicians to work in the north, but saw unanticipated consequences:
    • positive: changes in the community (e.g., more rentals, excitement in the community about the work being done at the university, culture of the community changed: a symphony was started in the community)
    • negative: other programs felt snubbed
  • Jonny wrote a book a while ago about how to evaluate unintended consequences (Evaluation in the Face of Uncertainty: Anticipating Surprise and Responding to the Inevitable)

Small Changes

  • “because of sensitive dependence, it may be impossible to specify an outcome chain”
  • e.g., sometimes programs evolve because of small things – e.g., because the program had time to do something that wasn’t in the original scope, or because the board agreed that something that wasn’t originally in scope still fit within the mandate

Unpredictability

A neat example of how difficult it is to predict the future is shown in this letter from Rumsfeld to G.W. Bush.

  • “the commonly accepted view of logic models and program theory may be less and less correct as time goes on”
  • there is debate over whether there are “degrees of complexity” (or if something is either complex or it is not”
  • some think that even if you start with a simple system that can be reasonably represented by a logic model, over time it will transition to complexity behaviour (he doesn’t believe there are “degrees” of complexity, so it’s not that a simple system smoothly transitions to a complex one

Network Effects Among Programs

Complexity
  • imagine you have one universe where:
    • two programs: one on malaria prevention and another one that is promoting girls education –> increased civic skills
  • and another universe where:
    • you have those two programs, but also other programs with goals around crop yields and road building = and all the programs interact with each other. E.g., if people are healthier (no malaria) and well fed (crop yield), you can work harder and increase economic development, which can feed back into the other programs, etc.
    • he thinks that this interconnected universe can have bigger effects over time
    • effectiveness can build over time with networked programs (whereas non-networked programs would just have the effect of the program and that’s it)
  • challenge: how do you evaluate this when programs (and evaluations) are generally funded for single programs (or at least within a single organization), but not across multiple programs in different areas
  • but there can be some programs that can spur change in all kinds of other areas of the system (e.g., ensuring everyone has a base level of education could –> increased civic engagement, increased health, increased economic development, etc.)

Joint Optimization of Unrelated Outcomes

verde amarelo
  • e.g., a program to try to decrease incidence and prevalence of HIV/AIDS
    • increase service –> decrease incidence and prevalence of HIV/AIDS
    • increase quality of service –> decrease incidence and prevalence of HIV/AIDS
    • decrease incidence and prevalence of HIV/AIDS –> better qualitity of life
  • this is a fine program model
  • all these outcomes are correlated
  • you pour a lot of money into this program – lots of people make career choices, intellectual capital goes there
  • so what happens to other things in the system?
  • less people, money, etc. to go to women’s health, other health services
  • so perhaps we see improvements in HIV/AIDS outcomes, but then you see worse outcomes in other areas of health
  • so instead of doing that, let’s jointly optimize unrelated outcomes
    • e.g., instead of trying to optimize just HIV/AIDS outcomes, but try to optimize health overall
    • of course, this is hard to convince people of this – how do you decide how much each different group gets
  • another example, you can drill people on reading to get them to do well on a test, but what if that makes them hate reading? Try to optimize that they do well enough on reading but also love reading
  • have you ever seen HUGE logic models – lots of elements and lots of arrows?
  • when you look at these, do you really think they are going to be correct? there’s lots of stuff that we don’t really know for sure; there are feedback loops that may or may not be true (feedback loops do tend to
  • famous picture of dealing with insurgent situation in Afghanistan – you look at it and think that it can’t possible be right on its whole – things like sensitive dependence, emergence, non-linear effects of feedback loops, etc., etc. aren’t accounted for here
  • it’s OK to have these big complex models, but it’s not OK to think that the whole model is true (even if you have data on every arrow within the model – because it doesn’t account for howcomplex systems behave). You can use the big model to look at pieces of it and think about how they relate to other parts of the model
  • he has a blog posting on “a pitch for sparse models” – if things happen in the “input” and “activity” realm, things will happen in the outputs/outcomes realm
  • he thinks that people can’t really specify the relationships in the level of detail that we usually see in big logic models (and he thinks it’s egotistical to think that we can do that).
  • but it’s not very satisfying to stakeholders to say “we can’t tell you anything about intermediate outcomes”
  • evaluators are complicit – we make these big models and stakeholders like it (and he says he is as guilty as anyone else at doing this)

Attractors

  • if you push something out of place and there is an attractor present, it will go back
  • e.g., rain that falls all ends up in the river, push a pendulum and ultimately it will end up back in the middle, planetary motion – gravity holds planets in their orbits, kids like playgrounds – kids will end up there, animals go to the waterhole
  • “explains why causal paths can vary but outcomes remain constant”
  • attractors are useful because:
    • lets you conceptualize change in terms of shape and stability
    • insight about program behaviour outside of stakeholder beliefs
    • promotes technological perspective: what will happen, not why

How do you decide if you should use complexity thinking in a given evaluation?

  • more work to incorporate complexity into an evaluation (than, for example, basing an evaluation on a simple logic model)
  • the evaluator – and the evaluation customer – should think about whether the value that is added by doing so is worth the extra work

For Further Reading

Jonny provided an extensive reading list. Here are some that caught my eye and I’m planning to check out:

  • Gates, E. F. (2016). Making sense of the emerging conversation in evaluation about systems thinking and complexity science. Evaluation and Program Planning, 59, 62-73 (PubMed)
  • Lawlor, J. A., & McGirr, S. (2017). Agent-based modeling as a tool for program design and evaluation. Evaluation and Program Planning. 65:131-138 (PubMed)
  • Morell, J. A. (2010). Evaluation in the Face of Uncertainty: Anticipating Surprise and Responding to the Inevitable New York: Guilford.
  • Morell, J. A. (2019). Revealing Implicit Assumptions: Why, Where, and How? https://www.crs.org/sites/default/files/report_revealing_assumptions.pdf
  • Walton, M. (2016). Expert views on applying complexity theory in evaluation: Opportunities and barriers. Evaluation, (Sage)
  • Williams, B., & Imam, I. (2007). Systems Concepts in Evaluation. Point Reyes, CA: EdgePress of Inverness. (online pdf)

Image Sources

  • Blue ropes photo posted on Flickr by Joe Lodge with a Creative Commons license.
  • Green and yellow tubes photo posted on Flickr by alex de carvalho with a Creative Commons license.
  • Spiky thing photo posted on Flickr by Manel Torralba with a Creative Commons license.

Written by cplysy · Categorized: drbethsnow

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