Examining students’ perspectives on gender bias in their work-integrated learning placements
Bibliographic record
Abstract
Work-integrated learning (WIL) affords students opportunities to apply skills and knowledge to practical work placements. Students potentially learn professional behaviours appropriate to their chosen industry sector. However, students may also face challenges they may not be prepared to navigate. One of these is gender bias due to assumptions about women and work, particularly within STEM sectors. This article presents findings from a pilot study that explores WIL students’ perspectives on gender bias related to experiences at their internship placements or other jobs. The findings suggest that the potential lack of gender neutrality within organizations such as WIL placements, is nuanced through an underlying bias around thinking about gender, women and work, and demonstrated through institutional structures such as branded recruitment campaigns or the individual micro aggressions of co-workers and supervisors. Further research needs to focus on the impact of gender bias on students’ sense of value within different organizations, and the strategies they employ to navigate bias. In the short-term, all students need tools to help them understand how gender is constructed within organizational processes and how to develop strategies to help them confront gender bias within the organizations in which they work.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".