An ethnic coalition: the Liberal Party of Canada and the engagement of ethnocultural communities, 1959-1974
Bibliographic record
Abstract
During the 1960s and 1970s the Liberal Party of Canada sought to engage ethnocultural communities in an effort to win federal elections. The author argues that the Liberal Party’s relationship with ethnocultural communities in Metro Toronto during the 1960s was characterized by indifference. Though it adopted a programme that encouraged the courting of ethnocultural communities, the Pearson-led Liberal Party showed limited interest in recognizing ethnocultural communities as a part of the party’s electoral coalition. The efforts of Andrew Thompson, the Liberal Party’s Ethnic Liaison Officer during the Pearson years, were separated from the rest of party’s organization and campaign structure. Prime Minister Pierre Trudeau ended Pearson’s lost decade and strengthened party bonds with ethnocultural communities. Trudeau welcomed ethnocultural communities to the Liberal Party, declared Canada as multicultural, and distributed patronage to leaders of non-English and non-French communities. This dissertation differentiates between groups and categories, and critically analyzes how people and organizations do things with categories. This dissertation argues that Thompson and the Liberal Party grouped ethnocultural communities as “ethnic groups” and “ethnic voters” in order to simplify diverse and unbounded peoples they did not understand.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.058 | 0.016 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".