Discerning Claim Making: Political Representation of Indo-Canadians by Canadian Political Parties
Bibliographic record
Abstract
The targeting of people of colour by political parties during election campaigns is often described in the media as “wooing” or “courting.” How parties engage or “woo” non-whites is not fully understood. Theories on representation provide a framework for the systematic analysis of the types of representation claims made by political actors. I expand on the political proximity approach—which suggests that public office seekers make more substantive than symbolic claims to their partisans than to non-aligned voters—by arguing that Canadian political parties view mainstream voters as their typical constituents and visible minorities, such as Indo-Canadians, as peripheral constituents. Consequently, campaign messages targeted at mainstream voters include more substantive claims than messages targeted at non-white voters. I conduct a content analysis of political advertisements placed during the 2004–2015 general election campaigns in Punjabi and mainstream Canadian newspapers. The analysis shows that political parties make more symbolic than substantive claims in both categories of newspapers; however, Punjabi newspapers contain slightly more symbolic claims than the mainstream ones. The Liberals and NDP make more substantive claims in Punjabi newspapers than the Conservatives.
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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 | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".