Racialized Pathways: How Online Job Boards Disadvantage Black Applicants
Bibliographic record
Abstract
The labor market is characterized by information asymmetry between employers and potential employees. Employers attempt to reduce this asymmetry by relying on various signals to infer an applicant’s competence and commitment. Using detailed administrative hiring records for entry-level employment in the nonprofit sector, I find evidence that Black job seekers who signal learning about an opening through online job boards experience poorer returns than Black job seekers who signal alternative non-networked recruitment channels (e.g., visiting a company’s website to learn about new employment opportunities). Analysis of job seekers’ resume data suggests that employers and Black job seekers face a double bind: online job boards attract highly qualified Black candidates, yet because these candidates are predicted to be more competent than others in the applicant pool, employers prioritize organizational fit when making callback decisions. The findings highlight how employers differentiate among non-networked recruitment channels and between racial groups in settings with weak employee-referral networks. The findings also rule in online job boards, a ubiquitous feature of contemporary labor markets, as a potential source of lower labor market returns for Black job seekers.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".