http://wol.iza.org/articles/anonymous-job-applications-and-hiring-discrimination
Bibliographic record
Abstract
Anonymous job applications have the potential to remove or reduce some discriminatory hiring barriers facing applicants from minority and other disadvantaged groups.When implemented effectively, anonymous job applications level the playing field in access to jobs by shifting the focus toward skills and qualifications.Anonymous job applications should not, however, be regarded as a universal remedy that is applicable in any context or that can prevent any form of discrimination. ELEVATOR PITCHThe use of anonymous job applications (or blind recruitment) to combat hiring discrimination is gaining attention and interest.Results from field experiments and pilot projects in European countries (France, Germany, the Netherlands, and Sweden are considered here), Canada, and Australia shed light on their potential to reduce some of the discriminatory barriers to hiring for minority and other disadvantaged groups.But although this approach can achieve its primary aims, there are also important cautions to consider. KEY FINDINGS ConsAnonymous job applications have the potential to reduce discrimination only when discrimination is high.Anonymous job applications may simply postpone discrimination to later in the hiring process.Blind recruiting may foil other positive measures to promote more diversity and can limit the scope for affirmative action.Suboptimal implementation of anonymous job application procedures can be costly, timeconsuming, labor-intensive, and error-prone.Context-specific information may be interpreted disadvantageously if the candidate's identity is unknown. ProsAnonymous job applications can prevent discrimination in the initial screening stage of recruitment.Anonymous job applications may boost job offer rates for minority candidates.Anonymous job applications signal a strong employer commitment to focus solely on skills and qualifications.Standardized anonymous job application forms are an efficient implementation method.Job applicant comparability may increase with the use of anonymous job applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.256 | 0.077 |
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".