CFB Esquimalfs French-Speaking Families: Social Networks and Linguistic Communities
Bibliographic record
Abstract
In the early 1970s, the Canadian Forces first bilingualism programs were aiming for an increased participation of French speakers in the military, mostly in services other than the infantry, where they had traditionally been more involved. French Canadians were particularly absent in the Canadian Navy, which was a stronghold of British tradition in the Canadian Forces. The policies which were put into place in order to implement the Official Languages Act were mostly directed at the Halifaxnaval base, in hopes of creating at least one bilingual navy base, while the west coast base of Esquimalt, near Victoria, was deemed to have too small a French-speaking population to justify French-speaking or bilingual units. However, the policy demanding a certain percentage of French speakers in all English-speaking units, paired with an increased recruitment of French speakers, resulted in a greater number of francophones in the Pacific fleet. This increasing number of Frenchspeakers coming to live in the Victoria region with their families, combined with the Official Languages Act, created a need to provide certain services in French, not only for military members, but also for their families. As such, although a small French-speaking community already existed in Victoria, it was the emergence of a large number of Francophone military families which justified the creation of a French-only school. Today, CFB Esquimalt's French-speaking military families form a community whose members can take part in many social networks, such as the anglophone military and civilian communities, and the Francophone military and civilian communities. In this paper, I present some preliminary conclusions based on fieldwork undertaken in CFB Esquimalt during the summer of 2006
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".