Language in job advertisements and the reproduction of labor force gender and racial segregation
Bibliographic record
Abstract
Job advertisements (ads) represent the first point of contact between employers and job seekers. By signaling characteristics expected of an ideal candidate, job ads "gatekeep" the labor force and configure its composition. Meanwhile, labor force composition can also shape the wording of job ads. This study develops a multidimensional inventory of gender and EDI (equality, diversity, inclusion) language in job ads. Applying this inventory, it adopts an instrumental-variable approach to disentangle the reciprocal relationships between gender/EDI language in job ads and labor force gender/racial composition. Drawing on the analysis of 28.6 million job ads in the United Kingdom in combination with labor force statistics between 2018 and 2023, the findings reveal three distinct mechanisms through which the bidirectional interplay between language in job ads and labor force composition (re)produces or disrupts labor force gender/racial segregation. They highlight both the benefits and limitations of intervening in the language used in job ads to help reduce labor force gender/racial segregation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".