Toward an Integrated Perspective of Minority Representation: Views from Canada
Bibliographic record
Abstract
While Hanna Pitkin's multifaceted conceptualization of political representation is well known and often cited, in practice, researchers still tend to examine each of her four main dimensions independently. The connective tissue and complex configurations of representation deserve more attention, for there is a serious risk of misspecification when one looks at a single, or even a pair of dimensions in isolation from the others (Eulau and Karps 1977; Schwindt-Bayer and Mishler 2005). With regard to minority representation, an integrated perspective brings into clearer focus dimensions of the concept that have been somewhat neglected. In particular, I argue that the symbolic and descriptive dimensions of minority representation are especially important in the Canadian context and should not be discounted as less meaningful than the substantive “acting for” dimension of representation. Following Pitkin, I also emphasize that much depends on the formalistic dimension of representation.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.054 | 0.015 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".