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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".