Language, Gender, and Work: Investigating Women’s Employment Outcomes in Ottawa-Gatineau’s Federal Public Service
Bibliographic record
Abstract
Women and men experience work differently owing to the gendered nature of work and workplaces, but there is limited insight into whether language and gender intersect to shape employment outcomes. This thesis project examines full-time employment in Ottawa-Gatineau to determine whether being French, English, or bilingual meaningfully influences employment status in the federal public service in terms of occupational attainment and employment income. A series of descriptive and inferential statistical analyses using the 2016 Canadian census are used to examine whether commuting patterns, occupational attainment, and annual employment income are significantly different across industrial sectors and between women and men, as well as between official language communities. The analysis reveals important differences in residential distribution between Anglophones and Francophones working in the federal public service as well as differences in commuting times, especially to suburban office locations. There are also important differences in occupational attainment and income attainment between women and men across official language communities, with women, especially francophone women, being more likely to occupy relatively low-pay administrative jobs in the federal public service compared to men or anglophone and bilingual women. In many ways, bilingualism in the federal public service is made real by the work of francophone women, although they are concentrated in some of the least-well paid occupations and stand to have ever more time consuming commutes as jobs are moved to suburban locations in Ottawa.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".