Supporting Data for: Women’s Contribution to Science and Technology through ICWES Conferences
Bibliographic record
Abstract
This dataset was created by Monique Frize, Claire Deschênes and Ruby Heap from data collected on each International Conference of Women Engineers and Scientists (ICWES), from ICWES-I to ICWES-XII. The dataset was first archived with the University of Ottawa Archives and Special Collections and has since been transformed into a dataset for analyses and preservation (see ReadMe file for quality-assurance procedures performed on the data); files are largely unchanged to accurately represent the historical record and the researchers’ data collection methodologies. A note left by the creators about its creation, content, and use, states: To obtain quantitative information, Excel files were prepared for each ICWES-I to ICWES-XII conferences, listing information taken from the programs and proceedings (the number and titles of the papers, presentations, activities for each ICWES sessions, the number of sub-topics by session (if any), the number of women, men, and unknown sex of the speakers, the ICWES region of the speakers and the pages where abstracts or papers can be found in the proceedings). The ICWES regions refer to 11 current regions of the world that were defined by the International Network on Women Engineers and Scientists (INWES) which was born out of the ICWES conferences.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.122 | 0.071 |
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".