The politics of multiculturalism reform in Canada : institutions, ideas and public agendas
Bibliographic record
Abstract
This thesis explores the dynamics ofpolicy reform and the management of etbnocultural diversity in Canada.It asks how changes to the Govemment of Canad&s multiculturalism program can be understood throughout periods of institutional reform and program rationalisation.In offering an answer, this thesis argues that public institutions serve as a vehicle for adjusting the terms of integration as well as contributing to our understanding ofcitizenship.This perspective pays particular attention to the role of ideas as an important determinant in policy making.At the same time, it highuights the institutional setting of Canadian politics that mediates conflicting interests and structures the flow of ideas.As such, rather than treating ideas and interests as separate and unrelated variables, this thesis explores how the two interact within an institutional context to explain both policy change and continuity.Our analysis is primarily policy centred, looking at the relationship between state and society to grasp the historical and organisational factors that shape policy decisions.In our examination of multiculturalism we identify three periods ofpolicy change: the 1970s, the 19$Os, and the 1990s.In each ofthese decades the debates and struggies in the Canadian polity resulted in significant institutional adjustments and practices.These events are analyzed as instances of policy leaming, wherein the state generated new understanding of problems and mobilized resources for administration.Across three decades of change, we find substantial continuity in the central role played by state actors in establishing the terms of ethnocultural integration.However, by the mid 1 9$Os the consensus among many ofthese same state actors had broken down.In its place, questions arose about the potentially destabilizing demands for ethnocultural and V representation rights in multinational societies.We find these concems to have served as vital adjuncts in the wider debate over state support for cultural diversity.By the 1990s, sufficient intellectual consensus in the form of neo-liberalism coupled with the growing criticism towards multiculturalism as a consequence of the national unity debate guided state action towards a new policy model.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.037 | 0.024 |
| Scholarly communication | 0.021 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".