Class Identity and Candidate Self-Presentation: Evidence from Canadian Provincial Elections
Bibliographic record
Abstract
Abstract Local candidates seeking to personalize their campaigns and build affinity with target voters may highlight particular aspects of their identities within campaign communications. One such aspect they may reference is their class background. For example, campaign materials frequently mention a candidate's occupational or educational background in order to build rapport with the electorate and indicate shared status, interests or values. This article compares the self-presentation of class identity among political candidates in the 2022 Ontario and Québec provincial elections. We code 976 online candidate biographies to assess how class background is referenced and examine the impact of variables such as party affiliation and riding demographics on self-presentation of class status. We further compare campaign biographies with data on candidates’ class backgrounds separately sourced from news reports and social media (LinkedIn). This allows us to determine which elements of class identity candidates choose to highlight, downplay or embellish in their campaign biographies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".