Breaking Barriers: How Libraries Can Better Support Female Architecture Students for Professional Success
Bibliographic record
Abstract
This study investigates the role academic libraries can play in narrowing the retention gap between the number of women graduating with a degree in architecture and the number of women currently in the architecture profession. Recommendations to libraries are made based on the existing literature and survey data. The findings of this study aim to propose solutions in the form of services and resources that libraries may provide to best support female architecture students. The findings shed light on four areas: 1. The importance of increasing students’ access to role models and mentors; 2. The need to bring more awareness to available academic tools; 3. The urge to diversify the material taught in the curriculum; 4. The value of sponsoring and hosting diversity-related events and programs. The proposed efforts will further empower women in architecture schools and can also benefit the broader community of minority groups by serving as a resource of recommendations for supporting the underrepresented communities in a profession.
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.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".