Breaking Intangible Barriers in English-as-an-Additional-Language Job Interviews: Evidence from Interview Training and Ratings
Bibliographic record
Abstract
Abstract This article reports a study that involved simulated job interviews with 27 high-proficiency English-as-an-additional-language (EAL) candidates and nine professional interviewers and that evaluated three conditions: a control group and two experimental groups (one receiving only personalized, training-focused feedback on interview skills immediately after the first interview, the other receiving both the same personalized feedback and a pragmatics-focused training session, also immediately after the first interview). As derived from the 2,106 scores generated, the quantitative results showed that both experimental groups significantly outperformed the control group. The qualitative results from content analysis of the interviewers’ 341 comments captured in video-stimulated recalls showed that various themes related to language ability featured most prominently in interviewer evaluations; the themes also differentiated above-average and below-average-rated candidates. The study underscores the extent to which communicative performance swayed interviewers’ judgements above other variables; these judgements in turn may prove a disadvantage for EAL candidates in their job interviews and thus merit the critical awareness and reflection of EAL candidates, interviewers, and trainers alike.
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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 | 0.121 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".