What do we mean by broadening participation? Race, inequality, and diversity in tech work
Bibliographic record
Abstract
Abstract In this article, I review the literature on race and racism in tech work and show that challenges related to increasing diversity and inclusion for racial and ethnic minorities are complicated and shaped by both immigration regimes and gender inequalities that do not impact all minority workers the same. I show that people of color are especially likely to be excluded from decision‐making leadership positions, limiting contributions that would shape the form and function of new technologies. Despite the complexity of these obstacles, I argue that addressing them is critical since the technology on which we increasingly rely may embed old racial inequity in an emerging technological landscape. Building from the existing literature, I show that (a) Black and Latinx workers are under‐represented numerically in tech work and those who do enter the field confront racial bias and (b) even though Asians are not numerically underrepresented, workplace practices, often supported by immigration policy and stereotype driven biases, interrupt full participation in decision making. I conclude by arguing that technological products reflect this lack of diversity in ways that further disadvantage communities of color.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".