A Great Candidate, I P(resume): The Role of Resumes and Cover Letters in a New Hiring Landscape
Bibliographic record
Abstract
Organizations commonly collect resumes and cover letters when hiring, despite their many potential drawbacks. Recent evidence suggests that pre-hire work experience has limited relevance for predicting future work performance, and jobseekers can now use web-based tools to compose many of their application materials. These changes to the hiring context call into question the extent to which organizations rely on resumes and cover letters to screen job applicants, and in particular, which features of applicants’ materials lead to success on the job market. The current study addressed these issues by tracking 183 students applying for full-time, paid jobs in their field, in connection with a post-secondary co-operative education program in Canada. We found that applicants whose cover letters and resumes were written with more detail, clarity, and structure secured substantially more interviews (relative to the number of applications submitted) and took less time to secure a position. Critically, the effects of resume and cover letter writing quality remained strong after controlling for content factors (work experience and achievement) and tailoring of materials, suggesting that organizations may be hiring applicants based on compositional factors such as writing quality that can be easily produced by new, widely available technologies like ChatGPT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".