The importance of language to minorities in urban areas : Acadian identity in Halifax, Nova Scotia
Bibliographic record
Abstract
This thesis explores how Acadians in Halifax Regional Municipality use French to index \ntheir identity, including the importance of language in doing so. Halifax, Nova Scotia is \nhome to an English majority as well as immigrants from other provinces and all over the \nworld. The Acadians are a community held together by their culture, language, historical \ncontext, and shared trauma from the 1755-1763 deportations. This thesis also explores \nhow Acadians are maintaining their cultural and linguistic identity here and the hurdles \nthey face in doing so. One such hurdle that this research suggests is that Acadian varieties of French may be at risk when confronted with language ideologies that can lead to assimilation and pressures such as a move towards a standard French. This can be seen in results from qualitative interviews with Acadians in Halifax and in statistics such as the decline in French as a first language which from 1971 to 2016 has gone from 3.5% to \n1.7% of the province’s population (Statistics Canada 2017b). This thesis applies linguistic \nand anthropological theories of identity to analyze the current situation of the Acadian \ncommunity in HRM and to look at the potential situations in the future. This research \nsuggests that French can be a tool used by Acadians, but it is not the only tool. \nAdditionally, you can identify as Acadian without having any knowledge of the French \nlanguage so it may be a more important tool to some than to others. Finally, this thesis \nsuggests that the Acadian identity as a whole is not at risk of being lost in the mix of other \nminority and majority identities that make up the city.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".