Talk matters: local council debates over electoral reform for Indigenous representation in Canada and New Zealand
Bibliographic record
Abstract
This study looks at recent city council debates over introducing Indigenous seats in Canadian and New Zealand municipalities, asking whether debate is respectful and focused on relevant issues, and agreements are decisive and consequential. Despite very different national contexts and local government systems, overall we find similar arguments across the two countries. And while deliberation is generally civil and principled among councils as a whole, individual councillors who voted against Indigenous representation tended to argue in ways that are potentially polarising. We relate our observations to wider arguments about the exclusion of urban Indigenous peoples from local policymaking and the importance of making rational inputs available for citizens’ judgement and decision.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.036 | 0.017 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".