ENGINEERING TEAM DYNAMICS: CONNECTING FRIENDSHIP NETWORKS AND ACADEMIC TRAJECTORIES
Bibliographic record
Abstract
Women are under-represented in STEM careers, especially engineering, as women are both less likely to enter engineering majors [1] and more likely to exit prematurely [2]. Women who leave engineering before achieving a career are then often underemployed [3]. We used an integrated research strategy to study how undergraduate classroom experiences might impact these gendered career trajectories, simultaneously modelling intrapersonal cognitions and interpersonal dynamics of students. Specifically, we studied cross-disciplinary engineering undergraduate teams over their first term in university. Importantly, this course was designed to have significant team interaction, and teams were assigned with the intention of reducing gendered barriers to participation and achievement. Our results revealed widespread gender equity (and the few observed inequities tending to shrink over the term). Further, we demonstrate specific intrapersonal and interpersonal factors that are linked to stronger academic trajectories (here operationalized as better final grades). Potential future interventions are also discussed.
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How this classification was reachedexpand
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 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".