EXAMINING GENDER AND NATIONALITY BIAS IN DECISION-MAKING BY ENGINEERING STUDENT TEAMS
Bibliographic record
Abstract
This study uses two-stage team quizzes to assess differences in team decision-making based on the factors gender and nationality. Over 200 teams in two different engineering design courses delivered using Team-Based Learning across five years were considered. In the two-stage quizzes, individuals first committed to their own answers, and then the team discussed the same questions and answered as a group. Cases where an individual was incorrect and the team adopted that same incorrect answer were used as a measure of influence of that individual on team decision-making (i.e., “pushing” behaviour by the individual). Similarly, cases where an individual was correct but the team adopted a different (incorrect) answer were used as a measure of lack of influence (i.e., “switching” behaviour by the individual). Overall, no significant gender or nationality differences were found in pushing behaviours. Male students and international students were found to be more likely to engage in switching behaviours. The overall differences in switching were modest (0.3-0.4% difference per question), but this translates to between 5 and 15 more male/international students engaging in switching behaviours in a typical 75- to 150-student course.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".