Two “Official” Languages of Work: Explaining the Persistence of Inequitable Access to French as a Language of Work in the Canadian Federal Public Service
Bibliographic record
Abstract
Canada’s official languages policy makes English and French the country’s official languages in federal institutions. The policy has succeeded in fostering equitable representation of both official languages groups in the federal public service and has improved capacities for the public service to serve the citizenry in its official language of choice. It is a puzzle however, that despite these advances, the Canadian federal public service continues to operate predominantly in English when both official languages on paper are equal languages of work. To explore this puzzle this dissertation asks: why, despite the promise of the Official Languages Act (OLA) 1969 for choice in language of work and the OLA 1988 that made the choice a claimable right, is there inequitable access to French as a language of work in the federal public service? Framed through a historical institutionalist approach and layering, this project analyzes the implementation of the official languages program in the federal public service from 1967-2013. This thesis argues that the implementation of the official languages program could not challenge the federal public service’s path dependency to operate predominantly in English. By analyzing the roles of actors and institutions that influenced the process, this dissertation finds that lack of structural change, inadequate managerial engagement and a false sense that official languages are engrained in the public service, can explain the persistence of English as the dominant language of work.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.036 | 0.038 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".