Women in Information Technology Initiatives in Canada: Towards Fact-based Evaluations
Bibliographic record
Abstract
For more than a decade, government, the private sector, associations, universities and schools have initiated projects aimed at increasing the participation of women in information technology (IT).Despite these efforts, female enrolments in computer science, engineering and IT programs, have plateaued or even declined.While many initiatives report high levels of participant satisfaction, their other impacts are less clear.This exploratory study provides a meta-analysis of the program designs and evaluations of 70 such initiatives available in Canada.It assumes that programs need clearly defined assumptions, objectives and evaluation processes in order to be effective.It explores the assumptions that underlie the programs, the program elements and the forms of evaluation employed.The paper concludes that there is little systematic evaluation of these programs and that there is a need both to question some of their underlying assumptions and to develop more robust, multi-layered approaches to evaluation.
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 | 0.286 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".