Racial Justice and Contentious Politics: The Impact of Racial Bias in Employment.
Bibliographic record
Abstract
Contentious politics focuses attention on collective actions and lobbying efforts to remedy injustices, particularly in the workplace, where racial inequalities continue to influence hiring procedures, promotions, and compensation. Despite anti-discrimination legislation like the Civil Rights Act of 1964, racial and ethnic prejudice continues to limit economic possibilities and exacerbate systemic disparities. Subtle kinds of bias, such as implicit and aversive racism, worsen the problem, influencing hiring decisions and maintaining socioeconomic disparities. Case studies from the United States, Brazil, and Malaysia show that racial bias in the workplace is a global problem, showing itself in behaviors such as neighborhood-based recruitment, cultural stereotyping, and implicit preference for dominant ethnic groups. Intersectionality exacerbates these processes, as those who face many forms of discrimination, such as race and gender, are marginalized even more. Emerging solutions, such as the use of artificial intelligence for blind hiring, diverse hiring committees, and broad policy changes, have the potential to reduce bias and promote inclusivity. However, establishing actual racial justice necessitates confronting both apparent and unconscious biases, as well as removing structural inequities entrenched in historical and systematic oppression. By promoting fair employment practices, societies may maximize the potential of a diverse workforce and promote equitable economic opportunities for all.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".