The Celebrating UNSW Women project: A strategic social impact project to drive gender equity, community and engagement
Bibliographic record
Abstract
Universities have a unique role in advancing social justice and fostering inclusive environments. This responsibility extends to creating spaces where all community members feel seen, recognised and celebrated. The visibility of role models for the broad and diverse university community is important for building acceptance and belonging. The ‘Celebrating UNSW Women’ project set out to address the historical underrepresentation of women across campus spaces at UNSW. Traditionally, campus buildings were named after men, however with 46 per cent of students1 and a significant portion of staff identifying as women; this lack of representation was seen as a missed opportunity. Prior to this project, only one building at UNSW was named after a woman, highlighting the gender disparity. Launched in March 2022, the project aimed to rectify this imbalance by renaming buildings and creating a physical presence on campus. This paper describes the initiative, led by UNSW Chancellor David Gonski AC and Deputy Vice-Chancellor Eileen Baldry AO, involved multiple stakeholders and was executed in three phases: renaming buildings, creating a physical and virtual trail and developing an equitable naming policy. This project not only enhanced representation but also promoted a sense of belonging and inspiration among women, contributing to long-term equity and leadership in the sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".