Election Campaigns as Information Campaigns: Who Learns What and with What Effect?” unpublished working paper
Bibliographic record
Abstract
During election campaigns political parties compete to inform voters about their leaders, the issues, and where they stand on these issues. In that sense, election campaigns can be viewed as a particular kind of information campaign. Democratic theory supposes that participatory democracies are better served by an informed electorate rather than an uninformed one. But do all voters make equal information gains during campaigns? Why do some people make more information gains than others? And does the acquisition of campaign information have any impact on vote intentions? Drawing on the combined insights from political science research, communications theory and social psychology, we develop specific hypotheses about these campaign information dynamics. These hypotheses are tested with data from the 1997 Canadian Election Study, which includes a rolling cross-national campaign component, a post-election component, and a media content analysis. The results show that some people do make more information gains than others; campaigns produce a knowledge gap. Further, the intensity of media signals on different issues has an important impact on who receives what information and information gains have a significant impact on vote intentions.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".