Service Responsiveness to Minority Constituents: A Field Experiment with Canadian Constituency Offices
Bibliographic record
Abstract
Abstract This research note presents the results of audit studies that were conducted with the constituency offices of provincial and federal elected representatives across Canada. We investigate whether individuals from ethnic minority groups, the LGBTQ+ community and French or English speakers are discriminated against when contacting their constituency office for administrative services. Survey experiments administered to both candidates of the 2021 Canadian election and a representative sample of Canadian citizens complement these studies. Our results indicate the absence of discrimination towards constituents from an ethnic minority or who identify with the LGBTQ+ community. We found, however, that emails sent in French were less likely to be answered by Members of Parliament (MPs) than those sent in English. Constituency offices of anglophone MPs and those representing ridings with a small proportion of francophones were significantly less likely to respond to French emails. A similar pattern, albeit more moderate, is observed among constituency offices of francophone MPs in response to English emails. The survey experiments show similar discrimination from citizens but less so from candidates.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".