Recognizing the Needs of Others: Municipal Candidates’ Intrinsic and Extrinsic Motivations to Support Immigrants and Religious Minorities
Bibliographic record
Abstract
This research examines the influence of political candidates’ personality dispositions and constituency characteristics on their assessments of the needs of immigrants and religious minorities. Previous research, drawing on data from citizens, links personality differences to attitudes toward diversity and support for minority communities. Extending this research to candidates during an ongoing election campaign, this study examines the interaction between constituency diversity and politicians’ intrinsic motivations to recognize the interests of immigrants and religious minorities. Using data from a unique candidate survey during the 2018 municipal elections in two large Canadian provinces (N = 1,073), results show that personality traits provide an intrinsic motivation, independent of candidates’ descriptive characteristics or the level of diversity in their constituency, to recognize a higher level of support needed by members of these diverse communities. More agreeable candidates are consistently more likely to acknowledge that more should be done for immigrants and religious minorities whereas the negative influence of conscientiousness on minority recognition is suppressed in highly diverse constituencies. The results extend previous research on personality and intergroup dynamics and situate candidates’ recognition of the needs of others as an important antecedent to political 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".