Bibliographic record
Abstract
This thesis examines how electoral maps, by shaping district demographics, impact the amount of attention allocated to minority groups within parliamentary debates. Drawing on theories of dyadic representation and past work on the impact of majority-minority districts, I argue that the amount of attention individual politicians pay to minority groups in single-member districts should increase as the population of that minority group in the district increases. I test this expectation by studying three minority groups in Canada: immigrants, aboriginal people, and francophones outside of Quebec. I measure attention using dictionary-based content analysis of every word said in the House of Commons in the 39th and 40th Parliaments (2006-2011). Using an individual-level model, I examine the impact of minority group demographics at the district level on the attention of individual legislators to minority groups. The expected positive relationship is consistent across all three groups, controlling for a variety of factors. I then show that this individual-level model permits an analysis at the institutional level. Using three counterfactual scenarios, including one that changes districtdemographics so that minority group members are maximally concentrated, maximally dispersed, or concentrated into majority-minority districts, I show that electoral district demographics have a relatively large direct eect on individuallegislators but a relatively small direct effect on legislative bodies as a whole. Instead, much of the impact on aggregate attention is indirect, coming through the election of minority representatives. With some elaboration, this approach couldbe useful for evaluating proposed electoral maps for their impacts on substantive minority representation across a variety of contexts.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".