‘Those people who chose us’: Discursive construction of identity and belonging in the context of Quebec’s 2018 provincial elections
Bibliographic record
Abstract
In the climate of the growing diversification of the ethnocultural landscape, Quebecers of French-Canadian background, often viewed as mistrustful of ethnic minorities, have been faced with the challenge of renegotiating the symbolic boundaries of what it means to be a Quebecer. This study investigates discursive construction of identity and belonging in a multi-text body of discourse generated in the context of the French-language party leader debates in the run-up to Quebec’s 2018 provincial elections, which brought to power the center-right Coalition Avenir Québec. A close textual analysis of discourse produced by the party leaders and by debate viewers commenting on the Facebook page of Radio-Canada during the debates’ live stream, following the research program of Critical Discourse Analysis, demonstrates an enduring ethnic bias in the conceptualization of Quebec identity by the dominant ingroup – Francophone Quebecers of French-Canadian origin – one that puts in jeopardy the inclusive, civic Quebec identity promoted in official discourse of the Quebec government.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.025 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".