“I think Canadians look like all sorts of people”: ethnicity, political leadership, and the case of Jagmeet Singh
Bibliographic record
Abstract
Abstract Research into the impact of a politician's sociodemographic profile on vote choice in Westminster-style systems has been hindered by the relative sociodemographic homogeneity of party leaders. Past research has focused mainly on the evaluation of local candidates in the American context, but given that elections in plurality systems are far less candidate-oriented , the evaluation of local candidates tells us little about the prevalence of affinity or discrimination in other contexts. This article investigates the effect of political leaders' ethnicity on political behavior by looking at the case of Jagmeet Singh in Canada, the first federal party leader of color in the country's history. While the literature has shown that the gender of leaders in Canada can matter, little is known about the attitudes of Canadians toward party leaders of color specifically. We are interested in the evaluations of Singh and his party, as well as the shifts in voting intentions between elections in 2015 and 2019. We uncover affinity-based behaviors from individuals who identify as Sikh, as well as a negative reception of Singh's candidacy in Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".