Examining students’ perspectives on gender bias in their work-integrated learning placements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it