Underrepresentation of Women in Canada’s Information and Communication Technology Sector: What Can We Learn from a Canadian Survey of Adult Skills?
Bibliographic record
Abstract
The proportion of women in Canada’s information and communication technology (ICT) sector is well below their proportion in other fields. One hypothesis for this phenomenon is that women may not have the “right stuff” to be heavily involved in ICT. We use basic ICT scores derived from Statistics Canada’s 2012 Survey of Adult Skills, which is the Canadian portion of the 2012 Organisation for Economic Co-operation and Development Programme for the International Assessment of Adult Competencies as a proxy for the “right stuff.” We find that, after controlling for appropriate covariates, Canadian women score higher than men on basic ICT skills. However, women with the same ICT test scores are much less likely than men to be employed in ICT occupations. We also find that hourly wages in ICT occupations are lower for women, but this wage gap is no greater than that in the general labour market. Given the current and projected shortages of ICT professionals, women represent a large, yet untapped, pool of talent for this sector.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".