Factors affecting compliance with reference check requests
Bibliographic record
Abstract
Abstract Structured reference checks have been demonstrated to be a reliable and valid predictor of job performance. However, the reference check is a unique assessment method for personnel selection in that a third party, the reference provider, is the source of the critical information on the candidate. If a reference provider is unwilling to either partially or fully comply with a reference check request, the usefulness of the reference check is likely to be compromised. This study examined the factors affecting compliance of the potential reference provider with a reference check request. The sample consisted of 905 U.S. adults who were recruited through the Prolific online crowdsourcing platform ( https://prolific.ac ). We asked the participants a series of questions related to their actual experiences over the past year in responding to reference check requests. We also included a between‐subjects scenario that examined whether job candidate performance, relationship to job candidate, and method of providing the employment reference would affect hypothetical compliance. The results of the study can be used to more deeply understand the factors that are related to the compliance of the reference provider, and as a result, more fully understand the value of the reference check for selection.
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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 | 0.022 | 0.171 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".