Bibliographic record
Abstract
The conventional wisdom is that election campaigns facilitate political learning. According to this so-called 'enlightenment thesis,' the contestation and noise of the campaign supplies voters with both the psychological motivation and informational resources to make better vote choices. In this way, election campaigns can be seen as helping overcome one of the central problems of modern democratic politics: chronically low levels of political knowledge across electorates and profound inequalities of political knowledge within them. This dissertation investigates the enlightenment thesis through analysis of the impact of election campaigns on learning in the domain of the economy. In particular, the dissertation examines campaign period change in, first, the quality of national economic perceptions, and second, the quality of the link between national economic perceptions and vote choice. The analysis proceeds through statistical analysis of survey data collected during ten national election campaigns across four countries (Canada, New Zealand, the United Kingdom, and the United States). The dissertation concludes that there is little evidence of campaign period learning in the domain of the economy. There is no general tendency for the campaign to improve the quality of national economic perceptions or to improve the quality of the link between these perceptions and the vote. Indeed, the campaign is as likely to frustrate as facilitate political learning in the economic domain. Furthermore, there is no general tendency for the campaign to offset pre-existing inequalities either in the quality of national economic perceptions or in the quality of the link between these perceptions and vote choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".