Du «Comment» participer à «Pourquoi» participer?: Analyse de la notion de participation dans le multiculturalisme canadien et l’interculturalisme québécois
Bibliographic record
Abstract
This paper presents a comparison between the two ways of managing immigration and diversity existing in Canada: multiculturalism (Canada-wide) and interculturalism (Quebec). While some authors (see Juteau, McAndrew and Pietrantonio 1998) argue that the two policies are more similar than different, others, like Gérard Bouchard (2011), argue that interculturalism is a rather unique model of managing diversity in Quebec. In this paper we aim to make a contribution to the debate by providing an analysis of the way both models understand immigrants’ participation in their new society. Indeed, both models seem to consider participation as having a central role in the process of integrating newcomers. However, we will show that the reasons put forward by interculturalism and by multiculturalism to explain their interest in participation are very different. On the one hand, multiculturalism recognizes the importance of participation as a way of protecting individual identities, in line with the spirit of liberalism. On the other hand, for interculturalism, the issue of participation seems to be more fundamental: not only is participation one of the founding pillars of the policy, but it is also consistent with the political, legal and historical characteristics of the province of Quebec.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".