How Cheap Talk Shapes Selection Decisions in Multi-Stage Evaluative Processes
Bibliographic record
Abstract
This paper examines how employers use cheap talk to infer a candidate’s likelihood to accept an offer. Employers in the nonprofit sector often perceive thank-you notes as a willingness to accept a job offer if given, even though these job market signals are thought to be too cheap for employers to seriously incorporate into their selection decisions. Integrating 18-months of participant observation of hiring teams and semi-structured interviews with nonprofit professionals, finding demonstrate that when thank-you notes are absent, employers discontinue preferred candidates despite their prior success in the screening process. Among nonprofit employers, high-SES candidates have a lower chance of receiving job offers relative to a low-SES candidates when failing to send a thanks-you email as this inaction increases concerns about offer acceptance. These findings challenge the assumption that employers ignore cheap talk in hiring decisions and reveal its critical role in shaping employers’ selection decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".