Researching Post-Industrial Political Behaviour With The National Occupation Classification In The 2019 and 2021 Canada Election Studies
Bibliographic record
Abstract
In the following note we describe coding of open-ended responses to respondent occupations in the Canada Election Studies 2019 (phone and online) and 2021 (online). 7904 unique entries were gathered from the three surveys and assigned 5- or 4-digit codes from the National Occupational Classification (NOC) using the information such as job and task descriptions and sample job titles from the NOC. 84%of entries were able to be matched to a 4-digit NOC code and 79% could be matched to a 5-digit code. The note concludes with a brief model of vote choice as a func-tion of whether respondents’ occupations are assessed to be in surplus or shortage. Tentative results suggests respondents facing wage declines or layoffs because their occupation project to be in surplus are less likely to vote for a right-wing party.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".