Resumes vs. application forms: Why the stubborn reliance on resumes?
Bibliographic record
Abstract
The focus of this Perspective article is on the comparison of two of the most popular initial applicant screening methods: Resumes and application forms. The viewpoint offered is that application forms are superior to resumes during the initial applicant screening stage of selection. This viewpoint is supported in part based on criterion-related validity evidence that favors application forms over resumes. For example, the biographical data (biodata) inventory, which can contain similar questions to those used in application forms, is one of the most valid predictors of job performance (if empirically keyed), whereas job experience and years of education, which are often inferred from resumes and cover letters, are two of the least valid predictors of job performance (among commonly used screening criteria). In addition to validity evidence, making decisions based on application forms as opposed to resumes is likely to help organizations defend against claims of discriminatory hiring while enhancing their ability to hire in a more diverse, equitable, and inclusive manner. For example, applicant names on resumes can lead to screening bias against members of identifiable subgroups, whereas an applicant’s name can be easily and automatically hidden from decision-makers when reviewing application forms (particularly digital application forms). Despite these convincing arguments focused on applicant quality and diversity, a substantial research–practice gap regarding the use of resumes and cover letters remains.
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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 | 0.049 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".