Time Won’t Give Me Time: Intersections of Racialized and Gendered Organization of Work in Tech
Bibliographic record
Abstract
Abstract Drawing on theories of gendered and racialized organizations and research on racialization and citizenship, we weave together an intersectional understanding of gendered racial meaning making in tech work. We focus on workers’ control over time, an important point of overlap between gendered and racialized organizations theories. Based on interviews with 85 tech workers who are either U.S. workers or Indian men in the United States with temporary non-immigrant visas (primarily H-1B visas), we examine the organization of work for tech workers with and without visa restrictions, focusing on control over time. We find that permanent U.S. engineers, mostly men, have considerably more control than Indian temporary workers, who have virtually none. U.S. managers have the most control, but because of the perception that they trade schedule control for less technical work, this work is feminized and devalued among engineers. Together, these conditions help to create a lower status, feminized managerial track and lower status, racialized but masculine technical track drafting the Indian workers in racially subordinated masculinity while preserving agency over time and status for technical workers with permanent legal status. These findings allow us to offer an intersectionally grounded revision of the abstract ideal worker concept.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".