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Link seems to redirect to the NASA history collection homepage. I think the one you want is possibly… https://historycollection.jsc.nasa.gov/JSCHistoryPortal/history/oral_histories/NACA/ChampineGR_5-1-08.htm
Unclear to those not in the US
I’d say if you’re including this sentence it needs more unpacking
recommend rewording. hard to read sentence.
Maybe “Critics called out Friedan, rightly, for….; it was multiply marginalized folks like Darden who the critics had in mind”. Maybe with a footnote to define multiply marginalized for folks.
as in “specifically Darden’s” or “Darden’s team’s”? Currently unclear to me
Why are you calling it “feminism”, then? (And does movement in one sector of social justice necessarily impact another?) Or should we be retiring “feminism” for the more inclusive, exacting term “gender equality”? Not willing to do that? Why not? <— The answer to that is what differentiates data feminism from data gender-equity. (Notwithstanding the subsequent bullet points…) So looking forward to this book, but hesitate to read anything “feminism” in lieu of “gender equality”.
Is the negative phrasing of all these points deliberate, supported by some theory? I would imagine positive framings are preferable, defining feminism by what it is rather than what it isn’t. If it was a rhetorical choice in order to emphasize “power” in the last point, I’m not sure if it’s worth it.
Because this paragraph is about metadata (and how hashtags highlight issues), it might be helpful to say more about how metadata and data visualization are potentially related strategies for making information more visible (which as you point out in the section on policing can be a double-edged sword).
It might be helpful to know something about how literacy practices at NASA (and the use of the bar chart specifically for other purposes) might have made this particularly persuasive information.
I really like this way of beginning the book. But I think a broad audience would be well served by a section, early on, that gives an explicit account of what you intend to do in the book and the stakes in doing so. For as I understand it, the book isn’t only about what data looks like from a feminist perspective; but rather what we risk societally by ignoring that perspective.
I really like this way of beginning the book. But I think a broad audience would be well served by a section very early on that gives an explicit account of what you intend to do in the book and the stakes in doing so. For as I understand it, the book isn’t only about what data looks like from a feminist perspective; but rather what we risk societally by ignoring that perspective.
There’s a beautiful blog post by Candice Lanius, “Your demand for statistical proof is racist.” https://thesocietypages.org/cyborgology/2015/01/12/fact-check-your-demand-for-statistical-proof-is-racist/
Another overarching critique I have is that, in some ways, you cede too much ground to data. Was it really “data” through which Champine knew of discrimination? Or was that the only language that was respected?
My partner (and former SAFElab member!) Maya Randolph connected this point to a famous quote from Toni Morrison, in a 1975 address at Portland State University:
“…the function of racism is distraction. It keeps you from doing your work. It keeps you from explaining over and over again, your reason for being. Someone says you have no language so you spend twenty years proving that you do. Somebody says your head isn’t shaped properly so you have scientists working on the fact that it is. Somebody says that you have no art so you dredge that up. Somebody says that you have no kingdoms, again you dredge that up. None of that is necessary; there will always be one more thing”.
I would say that for Champine and Darden, the function of data, and visualization, was a distraction. Alongside any discussion of redemptive uses of data, I think we should recognize that things need not be this way, and perhaps shouldn’t be this way. We should believe racism exists because people experience it. If there were any data that showed there was no inequality in some situation where people told me they experience it, I would sooner doubt the data and the modeling, because I know those are imperfect tools to get to what matters, human dignity. Fortunately, there’s a simple way to get to that: asking people and believing them.
Beautiful point, Momin - thank you.
An overarching critique I have (which is why I’m putting it here, and not in the chapters I’m actually reviewing) is that this book implicitly has a strong thesis, but I don't see it explicitly anywhere; stating it explicitly, and returning to it frequently, would help make the book feel more focused. I would phrase it as:
"Feminism reveals the politics of data."
An expanded version might add, specifically intersectional feminism, and "…and feminism tells us the implications of those politics, both current and future, and points to alternatives."
This comes from how there is a fair amount of defense of why feminism is uniquely equipped to reveal these politics, as well as a fair amount of argument about why data has politics. Explicitly tying them together makes the two points seem less haphazard, although it is still a challenge to introduce feminism in its generality alongside using it to make critiques.
Thanks Momin - this is a super helpful comment.
appointed. Clarence Thomas…
Your introduction is engaging and winds up well. I do wonder if the last sentence or two lead into a book about personalities, as does the focus on Darden. Won’t your reader want to know more about “data” and “visualization” and “feminism”? Maybe invite your reader to become a data feminist?
a rare moment where your narrative seems to invite unclear thinking about causality. So many causes of decline in enrollments by gender, besides open behaviors of exclusion
Yes, your bullet points work well! I would welcome you to connect to “knowledge is power.” Data are not knowledge, or even information. There might be experts on information, data theory who are concerned if you don’t signal that these are not simple concepts.
be more exact—many?
the shift to data surveillance could be more explicitly tied to the mission of your introduction. It now seems like a new topic of interest, almost unrelated to intersectionality, etc. above
This is key, and I like it.
Good explanation of intersc. concept. This may go too far, but I also see the idea as helpful for “reading” each identity as overdetermined by the others: hetero masculinity in US is troped as working class and Black, e.g.
In my view, this shouldn’t be capitalized as an adjective for the communities. But could stand corrected, of course!
In this paragraph, I am getting a strong sense that you are reaching an undergraduate or nonspecialist audience.
Already clear. Yes, even Virginia Woolf in 1928 and 1937 did understand the intersection of race/empire, class, and the individualist self-fulfillment of middle class women. She wrote about servants. In other words, many people writing about male or female rights would not do a good job in 1963 of seeing these intersections, yet socialist/materialist analysis here and there as long ago as the late eighteenth century had gone quite far toward what Crenshaw highlighted. I don’t object strongly to the simplified history here, but maybe add “Read More” or notes to good synopses of feminist thought?
As an introduction, I could use by now some signal by now why you are retelling this history, interesting and relevant as it is. This note suggests you WILL need to invoke a history of feminism but not claim that it has clear waves. OK, other than the title, does your reader know where we’re going? Why is the book focused on US? The history of women, feminism, and data, is not just a Black/White US history.
Maybe other readers as well as I begin to wonder about the sources of information? Will the book make notes/sources easy to find? I see the first note below.
Maybe worth noting that desegregation was probably technical and not “full.” Gender and class kept people apart in different parts of the facility.
I don’t know if this is the convention in the USA but in Canada “Indigenous” is capitalized when referring to a people
Thanks for this heads-up.
This is great!
This is great, but I’d also like to see what it is about. Is it about activism involving all genders and that aims at social justice in relation to the applications of data science?
It’s great to see what isn’t but I’d really love to see a definition about what it is too. Is data feminism about activism involving all genders and aiming at social justice in relation to data science?
Interesting juxtaposition of “organizations” to “people”. Is this intentional? Do we want to align with the idea that the actors are corporations and governments rather than the people that make up these institutions? This is what “often aided by” implies to me.
both the “stronger” and the positionality show up later in the bulleted section - I don’t know if it’s worth flagging up here…but it might be. I tend to restate things several times (signposting ftw) but that’s a stylistic choice.
I suspect that this could be even stronger, if you’d like. For example, rather than saying “ideas associated with intersectional feminist thought”, why not outline the political commitments of intersectionality as those of data feminism? So, for example, “a commitment to action, and attention to the networks of power that constrain the lives of black, brown, queer, GNC, and disabled people in particular ways.” Or: “…to action, and dismantling racism, seixism, ableism, and homophobia as interlocking systems of oppression” — I’ll also note that much of the text is situated in/draws from spaces that are Anglo-American dominated and it might be worth acknowledging that.
Some readers will find this easy to dismiss
Would we say ‘digitization’ then? It may be useful here to differentiate between the two terms.
perhaps its worth making it a two step process (as I’d argue it is historically), first the datafication and then the digitization. Or, a remediation and acceleration with modern digital datafication…
It would be great to see an expanded version of this definition. Also - if this is a key aspect of data feminism, I am wondering: are there other aspects (not mentioned in this definition) that you will come back with at a later point, with a different story?
It is great to read the story behind the concept.
To me the transition from the previous paragraph is slightly abrupt - I also am not sure that this clarification is absolutely necessary for the readers that I imagine this title will have.
Is there a reason you are not using this year’s numbers?
At this point I’m still not quite sure what you mean by “power.” You say this earlier: “the systems of privilege, on the one hand; and systems of oppression, on the other” But since the book is evidently focused on power, perhaps you could take a bit more space to explain what you mean by the term and how you see it working in relationship to data.
just a suggestion here - this would be relatively easy to address within the discussion of feminist intersectionality…
Echoing the point above: what it meant to be “treated like a secretary” in the 1960s might not be easily imaginable for undergrads today.
i really appreciate the time jump into the present here, but i wonder if/how the quantitative leaps in data collection and the qualitative changes in terms of automatic analysis could be fleshed out a bit more.
I object more. There is a missing link here, from computation to data. Yes, data and computation are inextricably linked modern history, but we had (quantitative) data without computation (e.g., Mesopotamian farming records that elites used to keep control), and computation without data (like simulation modeling; that does have output data, but doesn’t have to have input data, only parameters). The “data analysis” that you talked about earlier would have likely been of simulation outputs, which I would say is very different from personal data. I would need to be convinced that the issues around data on purchase histories and online behaviors has the same material threats as the lack of credit and respect for people analyzing the outputs of computed simulations. I could more easily be convinced by a thematic link (e.g., the same institutions who held back Darden and Champine are the ones creating the infrastructure of data), although that would be weaker, and I would still like to see made explicitly.
to me it is not entirely clear here whether and how Darden and Champine collaborated on producing the data / chart
so far the mentioned chart did not read as a collaborative effort
is it feasible to have Gloria Champine’s bar chart included here as a figure? in the cited interview it is only mentioned by her, but not depicted
succinct and to the point - quoteworthy!
i would really appreciate a few more references here – even if necessarily incomplete
this reads as if she continued her work in a similar fashion as before, yet the promotion did represent a significant career change, didn’t it?
i wonder whether this could be phrased a bit more empathically - considering that intersectionality was introduced more than 20 years later and that we’re now +50 years later looking back with our ‘intersectional’ glasses on
it is not necessarily clear what that ideological mission was
I love this! Really snappy/easy-to-understand description of the whole concept.
Wonder whether you want this line to also start with Data feminism… or maybe change the 3 points as they apply more generally to Feminism ?
I’m curious if this relates to the creator(s) of the data visualizations or their subject(s).
I question the use of “short-term inconvenience” here. Although the program may have ultimately resulted in important improvements for health and safety, losing a place to live can have devastating consequences for already vulnerable people (e.g., job loss,exposure to violence, forfeiture of personal property).
Will you define what you mean by data science? It’s sometimes used as a familiar catch all term for stats, math, ML/AI, etc.,, but also can have a more narrow technical definition. Even wikipedia’s definition seems to fall somewhere in between - https://en.wikipedia.org/wiki/Data_science
Personally, I wouldn’t generalize that all of the data visualizations I create required data science, but they do require analysis/stats. And data feminism is very relevant to even the simple charts and graphs that surface insight (but may require nothing more than plotting points with a pen and paper).
Agreed - it seems to me that this book (and the way that you use the term ‘data feminism’) covers topics that might not be considered as ‘data science’ necessarily..
the podcast Reply All has an excellent two-part episode (#127 and #128) with interviews with the guy who started designing them: The Crime Machine.
Maybe worth adding as a footnote?
in French, this means “pots”, as in pots and pans, and they have been used by women in demonstrations in Chile in the 1970s (and in Québec during the 2012 student strike)... maybe use an expression like "pre-cooked meals" or something like that? it will prevent francophones like me to pause and have to re-read the sentence a few times because it would make sense to talk about women and protests and casseroles in the same sentence, =P
is this an inside joke? do casseroles stand for friendship or camaraderie in US-American culture?
would love for this to be defined a little more — it’s not quite clear what you mean here.
This feature selection process is, of course, fraught with power dynamics too. Who made those choices? Would the model be different with different features? Not sure if it is too early in the book to dig into that, but it gets at the _power_ theme you introduce in the book’s purpose just after.
Thanks for this observation. We try to draw this out a little later but I think you’re right that it should be foregrounded here too.
This story of Darden (and then Champine) is fascinating and powerful. What a great way into this book! I wonder whether it would be worth bringing to the surface the way Darden is telling a feminist data story to her boss: a pool of workers has two starkly different sets of outcomes, with the more desirable outcomes correlating with gender rather than qualifications. (I hope I’m paraphrasing her case correctly.) Perhaps making that connection would help the reader see how Darden’s data-analytical habits of mind relate to her workplace advocacy: she is telling her own story not as an anecdote but as a consolidation and synthesis of the many stories she sees unfolding around her.
I like this way of contextualizing Darden’s work. Small point: I hesitated on “no less politically engaged” because I wasn’t sure what Darden’s work is being compared to. The comparison seems to point to the actions of the military (the most immediate referent of “those physical confrontations”), and political engagement might not be the best frame for that. Is there another way to say that these different activities share political consciousness, or to specify the politically engaged actors in the Detroit half of the comparison?
Not sure why Bell Hooks is written in all lower case when all the other names are not.
bell hooks (like danah boyd) chose a name for herself that is entirely lowercase
The Nike icon is a swoosh, not a swoop. Still almost works.
Shannon already mentioned reading Eubanks’s book—I think it could be useful here too. Eubanks does a great job of tackling this claim as a “middle class, liberal myth”, and that even “better data” and “better data practices” by “good” actors fail in the face of power. So maybe it’s more that data intersects with practices of the powerful, and not that data, itself, is power.
Maybe a nod to Virginia Eubanks’s work here, too?
A good idea. We have her in Chapter One, but might want to include a reference here too.
May need more explanation of what the job of “computer” was, since most people will not be familiar
Thanks for this feedback. We thought we were clear enough on this, but what additional information would you want to see?
I would resist referring to feminism as an “unfinished” project because I wouldn’t want readers who lack familiarity with feminist theories to think that the feminist project will ever be a “finished” or “completed” one. Feminist thought (at least radical, liberation feminist thought), compels us to always be critical of power and attentive to the mechanisms that sustain it. That will always be necessary work. “Ongoing” isn’t the best substitute, but I’d recommend using a term in that vain to describe feminist thinking, action, and work. It is never ending, and it will always be. I should also mention that when I use the term “liberation feminist thought,” I’m borrowing from Imani Perry’s ‘Vexy Thing: On Gender and Liberation.’ It’s an excellent text and if you haven’t read it yet, I would suggest it!
Thank you for this comment and the reference. I definitely agree about the idea that one could never complete or achieve feminism. I have not read that text (tho Lauren may have), but I’m going to go get it immediately!
Very well stated! It is important to link discussions of feminism directly to power so that those who have little orientation to feminist thought and practice don’t come away from the text believing that feminism is apolitical or about those who are not cis men attaining *equality* to cis men by accessing the privileges that have traditionally only been allocated to cis men. You have to tie any and all conversations about feminism to power and the dismantling of patriarchy.
This relates to a comment I made above, that I see the central thesis of this book is that “feminism reveals the politics of data.” This sentence would say how it is doing so: through an analysis of power.
However, I’m not sure if “data science” is the right target. An earlier comment talks about the need to define data science; as somebody who is in that world, I would say most of the definitions are aspirational and political, descriptively data science is mostly applied machine learning (and machine learning I might quickly described as “megalomaniacal statistics”: same underlying machinery, but used in different ways). Just as applied statistics focuses on exploratory data analysis and visualization and working on serving a domain partner/client more than statistics without that “applied” qualifier, and will usually re-use existing models rather than developing custom ones, data science involves the same orientation compared to machine learning: they will seldom build their own models or even do much fine-tuning of existing model (unless they are machine learning people who turned to applied work), but will know what sorts of existing techniques to use.
If you seek out a definition of data science, I would suggest only using something consistent with this (at a minimum!). I unfortunately don’t have good things offhand, but I trust Rayid Ghani as one person to look to (http://www.rayidghani.com/is-data-science-a-real-science), although even things from the program he started, Data Science for Social Good (e.g., https://github.com/dssg/hitchhikers-guide, see what makes a good data scientist for social good) and which both I and Ben Green did in different years, are aspirational.
But much of the actual target of your critique is the data economy and institutions that collect data and deploy machine learning to use it. There are critiques to be made about how modeling frames the world, and the implications of certain modeling mechanisms (which I can share a whole literature on), but that’s not what’s in this book. There’s also a critique to be made about the complicity of people who are “data scientists” in larger institutional structures, which I one thing I see in Ben Green’s work, and I’m not sure that appears later in the book.
I agree with the concerns listed below, but to the best of my knowledge, there are no place-based predictive policing systems that are known to use arrest locations.
I’ve spoken with a couple folks who’ve written about — or are writing about — predictive policing, and they agree that it’s not clear if arrest location are used, or are likely to be used in future platforms. So, it’s probably best to substitute another variable here.