Ethnoracial Identities and Political Representation in Ontario and British Columbia
Bibliographic record
Abstract
Political representation of minority groups is an important aspect of modern societies. Are our parliaments generally reflective of the people they serve? In this article, the authors use the results of two recent Canadian provincial elections (Ontario, 2018 and British Columbia, 2017) to explore whether majority and minority groups are proportionally represented in legislatures and to probe some explanations as to why these groups may be over-represented or under-represented. They address notions of residential concentration and the assumption of ethnic affinity to partially explain where ethnoracial minority candidates are likely to be elected. In contrast to past work which has found a general under-representation of minority groups, this analysis finds some nuance. Some racialized groups, notably Chinese Canadians, appear to be proportionally more under-represented than others. The authors explore a range of arguments to explain this finding. In conclusion, the authors highlight two key findings from this research. First, they suggest it is difficult to make the case that being part of a racialized group has a negative impact on political representation at the provincial level – at least currently in two provinces with large racialized populations – without introducing nuance that subdivides ethnoracial minority groups. The second finding is conceptual: ethnic affinity cannot solely predict voting behaviour. The authors contend that the concept must be broadened to include centripetal ethnic affinity and transversal ethnic affinity.
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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".