Interventions to Improve the Candidate Experience of Structured Videoconference Interviews
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intense competition for talent has led to increased organizational focus on improving how applicants perceive and respond to selection tools. Because of the recent increased use of technology in selection, we tested whether modifying aspects of videoconference interviews could improve applicant reactions. We tested two interventions—structured rapport building and question provision—with 205 applicants applying for a research assistant position. Applicants were randomly assigned to either an experimental condition (rapport or question provision) or the control condition and participated in a structured videoconference interview, followed by a survey. Structured rapport building had no significant effect on applicant reactions. However, question provision improved applicants’ perceptions of overall fairness and chance to perform—but not their reported anxiety, relative to the control condition. Question provision appears to be a simple and cost-effective intervention that could be used in a structured videoconference interview context to help to improve the applicant reactions.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it