Millions of people use social media to navigate identities too complex for single analytical frames like race, class, gender and sexuality to fully capture. We are messy and complicated and we seem to want our digital tools to reflect that. But, intersectionality was never intended to only describe lived experiences. Intersectionality was to be an account of power as much as it was an account of identities (Crenshaw 1991). Here, the potential of intersectionality to understand the reproduction of unequal power relations have not yet been fully realized.

In brief, intersectionality is one of those rare social theories to combine precision of theoretical mechanisms with broadness of method (Lykke 2011). That combination has served intersectionality’s diffusion through social sciences and humanities quite well. It has also created tensions about what intersectionality really means and how best to measure it (or, if it should be measured at all!).

In the black feminist tradition, examining the points of various structural processes where they most numerously manifest is a way to isolate the form and function of those processes in ways that can be obscured when we study them up the privilege hierarchy (Hill Collins 2000). Essentially, no one knows best the motion of the ocean than the fish that must fight the current to swim upstream. I study fish that swim upstream.

A roaming autodidact is a self-motivated, able learner that is simultaneously embedded in technocratic futures and disembedded from place, culture, history, and markets. The roaming autodidact is almost always conceived as western, white, educated and male. As a result of designing for the roaming autodidact, we end up with a platform that understands learners as white and male, measuring learners’ task efficiencies against an unarticulated norm of western male whiteness. It is not an affirmative exclusion of poor students or bilingual learners or black students or older students, but it need not be affirmative to be effective. Looking across this literature, our imagined educational futures are a lot like science fiction movies: there’s a conspicuous absence of brown people and women.

Intersectionality theories or methods have not yet been fully realized in the study of digitality and education, a critical institutional axis of social stratification.

The privatization of critical institutional arrangements like higher education is a serious challenge for digital sociology’s focus on studying inequalities. And, to keep expenditures low and profits high, faculty at for-profit colleges largely do not have a research imperative and physical campuses have few unstructured spaces for observation. Financial imperatives of privatized public goods shifts institutional responsibility from knowledge production to market penetration, privileging market competition over social inquiry.

Social media platforms afforded students who are rendered invisible in analysis because of privatization and intellectual enclosure to speak their experiences into legibility.

However, to move beyond giving voice to uncovering the ways in which power and privilege are often unmarked in social science research (Bonnett 1996; Zuberi 2008) intersectionality demands that we examine process and power relations. That is part of intersectionality’s political imperative.

Intersectionality theory argues that narrative methods de-centers privilege in rational actor theories. Therefore, I conceptualized the social media data I collected as autoethnographies rather than content. While content can absolutely be analyzed as narratives, they are most often analyzed as quantitative abstractions or without attention to qualitative differences in the power that frame content. In contrast, ethnographic data’s imperative is to situate meaning among various relational dynamics like power, privilege and social location (Ellis and Bochner 2006). Autoethnographies resist hegemonic sensemaking paradigms by centering self-authored texts and the co-construction of meaning. These theoretical imperatives, mechanisms and methodological choices are consistent with black cyberfeminism’s focus on intersectionality and unique characteristics of digitized social processes.

Source: Black Cyberfeminism: Intersectionality, Institutions and Digital Sociology by Tressie McMillan Cottom :: SSRN

“We’re finally getting the stage where a large portion of the population can’t really ignore the fact that they’re using free services in return for pervasive and always-on surveillance.”

Source: Every part of your digital life is being tracked, packaged up, and sold | Doug Belshaw’s Thought Shrapnel

Behaviorism and surveillance at school. Behaviorism and surveillance at work.
The making of the automatron class.

“And that, increasingly, is the dividing line in modern workplaces: trust versus the lack of it; autonomy versus micro-management; being treated like a human being or programmed like a machine.”

Source: We fear robots at work, but robotic jobs for humans are awful too | Gaby Hinsliff | Opinion | The Guardian

And that, increasingly, is the dividing line in modern workplaces: trust versus the lack of it; autonomy versus micro-management; being treated like a human being or programmed like a machine. Human jobs give the people who do them chances to exercise their own judgment, even if it’s only deciding what radio station to have on in the background, or set their own pace. Machine jobs offer at best a petty, box-ticking mentality with no scope for individual discretion, and at worst the ever-present threat of being tracked, timed and stalked by technology – a practice reaching its nadir among gig economy platforms controlling a resentful army of supposedly self-employed workers.

There have always been crummy jobs, and badly paid ones. Not everyone gets to follow their dream or discover a vocation – and for some people, work will only ever be a means of paying the rent. But the saving grace of crummy jobs was often that there was at least some leeway for goofing around; for taking a fag break, gossiping with your equally bored workmates, or chatting a bit longer than necessary to lonely customers.

The mark of human jobs is an increasing understanding that you don’t have to know where your employees are and what they’re doing every second of the day to ensure they do it; that people can be just as productive, say, working from home, or switching their hours around so that they are working in the evening. Machine jobs offer all the insecurity of working for yourself without any of the freedom.

The debate about whether robots will soon be coming for everyone’s jobs is real. But it shouldn’t blind us to the risk right under our noses: not so much of people being automated out of jobs, as automated while still in them.

Source: We fear robots at work, but robotic jobs for humans are awful too | Gaby Hinsliff | Opinion | The Guardian

…he divides his colleagues into three different types:

1 Compassionate
2 Dispassionate
3 Compassionate

The first group suffer burnout, he said. The second group survive but are “lousy”. It’s the third group that cope, as they “care for patients without sacrificing themselves on the altar of professional vocation”.
What we need to be focusing on in education is preparing young people to be compassionate human beings, not cogs in the capitalist machine.

Source: The spectrum of work autonomy | Doug Belshaw’s Thought Shrapnel

“Which teacher has time to make custom books for his or her class? One of the things I’ve become concerned about is the number of items we tend to keep adding to a teacher’s plate. They have to manage a classroom of 15-30 kids, understand all of the material they teach, learn all of the systems their school uses, handle discipline issues, grade papers, and help students learn.”
“When do we start to take things off of a teacher’s plates? When do we give them more hours in the day? Whatever Apple envisioned in 2012, it’s clear that did not play out.”

Source: Making The Grade: Why Apple’s education strategy is not based on reality | 9to5Mac

I added selections from “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” and “Opinion | The ‘Roseanne’ Reboot Is Funny. I’m Not Going to Keep Watching. – The New York Times” to “To the family Trumpists”.

“Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.”

Source: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 

They act as if love can protect the most vulnerable members of their family from the repercussions of their political choices. It cannot.

Source: Opinion | The ‘Roseanne’ Reboot Is Funny. I’m Not Going to Keep Watching. – The New York Times

You are the family bigots. That is your legacy. That is how you will be remembered.

Source: To the family Trumpists

“Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.”

Source: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy