Indigenous Diffuse Support and Descriptive Representation in the Canadian House of Commons
Bibliographic record
Abstract
Abstract Do Indigenous peoples in present-day Canada display lower levels of diffuse support than non-Indigenous settlers? Given settler colonial relations (both historic and contemporary) and Indigenous peoples’ own political thought, we can expect that Indigenous peoples would have even lower perceptions of state legitimacy than non-Indigenous peoples. However, there are conflicting expectations regarding whether the descriptive representation of Indigenous peoples in settler institutions is likely to make a difference: on one hand, Indigenous people may see themselves reflected in these institutions and consequently feel better represented; on the other hand, these forms of representation do not challenge the underlying colonial nature of these institutions. Using data from the 2019 and 2021 Canadian Election Studies, our statistical analysis demonstrates that: (1) diffuse support is significantly lower among Indigenous peoples than non-Indigenous peoples, including people of color; (2) Indigenous respondents across multiple peoples have similarly low levels of diffuse support, and (3) being represented by an Indigenous Member of Parliament does not change the levels of diffuse support among Indigenous peoples. Overall, our research highlights the outstanding challenges to achieving reconciliation through the Canadian state and points to ways large-N analyses may be made more robust.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".