Building an Ethnic Coalition?: The Liberal Party of Canada, Ethnocultural Communities and the 1962 and 1963 Federal Elections in Metro Toronto
Bibliographic record
Abstract
In the early 1960s, the Liberal Party employed Toronto-area Member of Provincial Parliament Andrew Thompson as its Ethnic Liaison Officer. Under Thompson’s operation, the Liberal Party undertook efforts to engage ethnocultural communities to win the 1962 and 1963 federal elections. This article compliments existing scholarship on federal elections and shows exactly how the Liberals coordinated their efforts to appeal to ethnocultural communities. Though the Liberal Party targeted these communities, their efforts failed to include them in the Liberal Party, the election process, and the broader parliamentary system. In this sense, ethnocultural communities were marginalized in this political process. The analysis in this article also explores how the Liberals and the Conservatives homogenized “ethnic groups” in specific moments and utilizes the scholarship on the invention of ethnicity, particularly Rogers Brubaker’s idea that ethnicity is an event.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.033 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".