Toward People’s Community Control of Technology: Race, Access, and Education
Bibliographic record
Abstract
This field review explores how the benefits of access to computing for racialized and minoritized communities has become an accepted fact in policy and research, despite decades of evidence that technical fixes do not solve the kinds of complex social problems that disproportionately affect these communities. I use the digital divide framework—a 1990s policy diagnosis that argues that the growth and success of the internet would bifurcate the public into digital “haves” and “have-nots”—as a lens to look at why access to computing frequently appears as a means to achieve economic, political, and social equality for racialized and minoritized communities. First, I present a brief cultural history of computer-assisted instruction to show that widely-held assumptions about the educational utility of computing emerged from utopian narratives about scientific progress and innovation—narratives that also traded on raced and gendered assumptions about users of computers. Next, I use the advent of the digital divide framework and its eventual transformation into digital inequality research to show how those raced and gendered norms about computing and computer users continue to inform research on information and communication technologies (ICTs) used in educational contexts. This is important because the norms implicated in digital divide research are also present in other sites where technology and civic life intersect, including democratic participation, public health, and immigration, among others. I conclude by arguing that naïve or cynical deployments of computing technology can actually harm or exploit the very same racialized and minoritized communities that access is supposed to benefit. In short, access to computing in education—or in any other domain—can only meaningfully contribute to equality when minoritized and racialized communities are allowed to pursue their own collective goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".