Rater Correction Processes in Applicant Selection Using Videoconference Technology: The Role of Attributions<sup>1</sup>
Bibliographic record
Abstract
Increasing global competition for the best employees has resulted in a significant increase in the recruiting and selection of geographically dispersed applicants. Innovative telecommunication technologies (e.g., videoconferencing) have provided a means to interview distant applicants at relatively low cost, compared to face‐to‐face interviews. However, some have suggested that interviewer ratings could be affected by the use of communication media to conduct the interview. In the present laboratory study, we tested a model of rater decision processes to help explain a mechanism for inflated ratings of videoconference‐based applicants. Participants who believed that they were making judgments for a real selection process rated simulated videoconference or face‐to‐face interviews. Raters who perceived interview media to be lower in richness were more likely to make external attributions for the applicant's performance, and consequently rated him more favorably.
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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 | 0.169 | 0.555 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".