Évolution du traitement des enjeux relatifs à l'immigration et à l'integration des immigrants dans le discours partisan au Canada : analyse de contenu des plateformes électorales de 1993, 1997, 2000 et 2004
Bibliographic record
Abstract
This thesis studies the discursive behaviour of Canadian federal political parties with regards to immigration and integration issues. It seeks to test the empirical acuity offered by brokerage and issue ownership theories to explain the parties' electoral strategies in these domains. It examines the evolution of partisan discourse in relation to these themes over time, with special attention paid to the merger of right parties. It also studies the impact of certain real-world events, such as the referendum on Quebec secession in 1995 and the terrorist attacks of September 2001, on party positions. It thus proposes a quantitative and qualitative content analysis of five major parties' discourse, focusing on the various positions held by each of them on the issues of immigration and integration in their respective 1993, 1997, 2000, and 2004 election platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".