Through the Looking Glass: Employment Interviews from the Lens of Job Candidates
Bibliographic record
Abstract
Job interviews are of crucial importance to the job search process. As a result, recent years have witnessed a considerable amount of research on job interviews from the perspective of candidates. While this research has provided valuable insight into candidate reactions, it has yet to have a strong impact on the actual behaviors of job candidates and organizations. Thus the goal of the current chapter is to bridge the gap between empirical knowledge and applied practice in job interviews. To accomplish this objective we first present a framework for understanding the interview process that is grounded in theoretical and empirical research. The focus of this framework is whether candidate characteristics (e.g., gender, age), behaviors (e.g., impression management, communication style), and reactions (e.g., anxiety, justice) have an effect on important interview-related outcomes, such as interview performance. This is followed by a comprehensive discussion of research relevant to each section of the framework, including impression management, the first handshake, interview anxiety, and other predictors of interview success. Implications for research and practice are discussed and a checklist for practice is provided. We conclude by highlighting how properly conducted interviews can simultaneously serve the best interest of both job applicants and organizations.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".