Replication Data for: Fulling Discontent: The Politics of Carbon Taxation in Canada
Bibliographic record
Abstract
Research hypotheses: Trade-sensitive and carbon intensive sectors and workers are more likely to reject carbon taxation than those who are sheltered and cleaner. Individual level dataset: Data from the 2019 and 2021 Canadian Election Study (CES). Assess the relationship between respondents' employment profile and their views on carbon taxation and environmental policy more broadly. Respondents are geocoded at the provincial level and are asked to specify in writing their occupation. Manually coded respondents' 2-digit NAICS occupation classification and matched them with the trade and emission-intensity data. Employ modified specification to account for job-specific trade-sensitivity (1) and carbon-intensity (2). Data on sector-specific GHG emissions and exposure to trade are provided by Statistics Canada. Ecological data: To account for job and constituency-specific carbon emissions, I leverage Canada's GHG inventory compiled by Statistics Canada. The inventory breaks down yearly emissions by NAICS industrial sectors at the provincial level. Using Statistics Canada's employment data, I first divide our emission data by industrial and provincial employment profiles in order to account for each jobs's carbon intensity at the provincial level. I then match these data with constituency-specific employment profiles to account for overall emissions at the riding-level. Insofar as employment data at the constituency-level are drawn from the national quinquennial census of 2011, 2016 and 2021, I perform linear extrapolations in order to balance the dataset, thus covering the full 2015-2021 period. Another factor behind "carbon layoffs'' is exposure to trade. I first leverage Statistics Canada's trade flows data. Trade flows are provided both at the industry-level on a province by province basis, whereas employment profile is broken down at the constituency-level. Following Yamazaki (2017), I build a constituency-specific trade-sensitivity index. Data on constituency-level sociodemographic characteristics --unemployment, minority share, BA share, car commuters' share, household income and population density-- all come from the Canadian Census. Linear extrapolations are performed in order to balance the dataset. I leverage Election Canada's constituency-level data to account for the vote share captured by anti-carbon tax parties (the Conservative Party and the People's) between 2015 and 2021. Regional gas prices stem from both Kalibrate and Statistics Canada. The yearly share of non-carbon energy sources (hydro, renewable, nuclear) in the provincial electricity mix is provided by Statistics Canada. Finally, constituency-level belief in the existence of anthropogenic climate change comes from the 2016 and 2018 Canadian Climate Opinion Maps, from the Yale program on Climate Change and Communication.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.157 | 0.039 |
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".