Environmental Issues in Recent British and Canadian Elections
Bibliographic record
Abstract
ENVIRONMENTAL ISSUES IN RECENT BRITISH AND CANADIAN ELECTIONS The 2019 elections in Britain and Canada illustrate the difficulties in communication between a concerned public and prospective office-holders on the most critical set of issues of our times. An increased level of public awareness and concern about the state of the environment has been expressed in public opinion polls, social movement activity has increased, and Green parties have expanded their appeal. Despite these developments in recent years, environmental issues have not been able to exert a major impact on individual voting behaviour in elections, or on overall election outcomes. Issues related to the environment are usually treated, by both politicians and the public, in valence terms. Valence issues are ones upon which there is broad consensus about the goals of public policy, and political debate focuses not on "what to accomplish" but rather on "how to do it" and "who is best able." Regarding the environment, general formulations like global warming and climate change prompt politicians to offer concerned rhetoric and engage in virtue signaling, but specific policy proposals are often absent. This paper examines four reasons why environmental/climate change issues did not have a major impact on the 2019 Canadian and British elections. First, environmental concern in society at large was imperfectly translated into election issues. Second, the major political parties produced inadequate and unconvincing environmental manifestos. Third, environmental issues were not central to most voting decisions. Fourth, environmental issues had limited impacts on election outcomes.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".