EXPLAINING GEOGRAPHIC VARIATION IN ELECTORAL BEHAVIOUR: LOCAL ENVIRONMENTS AND CANADIAN VOTING
Bibliographic record
Abstract
Geographic variation in election results is an enduring feature of modern democracies. Much of this variation is due to the distribution of individuals across geography, but some remains after researchers control for relevant individual characteristics and attitudes. Otherwise similar individuals living in different places reason differently about politics and make different political choices. Features of citizens' local environments affect their political behaviour. This dissertation uses and extends theory from political psychology to specify when and how local information and local interests will be integrated in citizens' political reasoning. Survey data from the Canadian Election Studies of 1993 and 1997 are merged to census, economic, and political data describing citizens' local environments. This data is used to estimate the influence of these local factors in four specific ways. Local economic conditions are found to have little influence on provincial or national economic evaluations; instead, they influence government approval directly. Predictably specific features of the local social and economic environment are found to influence opinion on a number of electoral issues, and have no effect on issues that have no obvious local referents. Political party leaders' easily identifiable characteristics, especially their geographic affiliation, are found to strongly influence voting behaviour over and above party identification, issue positions, and economic perceptions. Finally, intrinsically local factors, the incumbency and spending of candidates for parliament, do not influence voting behaviour. These influences are as strong or stronger for those well-informed about politics as for those with little political knowledge, and just as strong for those who do not discuss politics as for those who do. Thus the influence of the local environment is not limited to the effects of social interaction-structural and global features of the locale influence how citizens determine what is in their interest. Citizens, irrespective of their political sophistication and engagement, are localistic: they care more about the fortunes of their locale than other areas. Localism is under-appreciated as a driver of political behaviour and should be invoked in explanations of geographic variation in election results.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".