The Effect of Interviewers’ Genders on the Quantity and Quality of Their Interviewees’ Output: A Comparative Inquiry among Saudi Students
Bibliographic record
Abstract
Gender segregation is widely established in Saudi Arabia, and this may affect the interviewing dynamic in conducting spoken tests. Such an effect could pose significant consequences for interviewees; for example, it might affect their ability to obtain high scores in the speaking sections of standardized tests (e.g., the IELTS). This could prevent them from enrolling in universities in English speaking countries (e.g., United States, United Kingdom, Australia, New Zealand, and Canada) as well as other universities that require such tests for enrollment purposes. As such, this study attempts to explore whether the gender of an interviewer can affect how a respondent forms their answer in terms of both depth and length. In this study, two interviewers (a male and a female) interviewed six Saudis of both genders, comprising a total of 12 interviews. The results showed that when both the interviewer and the interviewee were of the same gender, interviewees’ responses tended to be lengthy, and they were more likely to expand their response to other related topics. On the other hand, when both the interviewer and the interviewee were not of the same gender, brevity and/or avoidance characterized their answers.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".