Preaching to the Choir: Models of Citizenship and Concepts of Democracy in Reception of ‘Get Out the Vote’ Posters
Bibliographic record
Abstract
This dissertation reports the findings of research that studied the reception of Get Out the Vote posters, by Canadian citizens of voting age, via an interdisciplinary theoretical framework. The study’s main finding is that the reception of Get Out the Vote posters is better explained by an intersection of personal Concepts of Democracy (Saward, 2003) and personal Civic Models Concepts of Citizenship (Dalton, 2009) than it is by audience reception theories or voter turnout theories separately. The study begins with theoretical and methodological reviews in chapters one through three. Chapter four explores audience reception of posters vis-a-vis demographic factors using Hall’s Encoding/Decoding model (1980), Morley’s Reading Types model (1980) Tomkins’ Affect Theory (1995) and the photo elicitation data generation method (Harper, 2002). Chapter five explores the role of Concepts of Democracy (Saward, 2003) in meaning-making vis-a-vis reception of visual elements. Chapter six explores the role of Civic Models Concepts of Citizenship (Dalton, 2009) in meaning-making vis-a-vis reception of slogans. Chapter seven creates a system of four Political Character Types from the intersection of the two concepts: • “Choir members” who tend to internalize the intended message positively. • Libertarians who oppose the intended message or negotiate its meaning negatively. • Activists who negotiate their meaning of the intended message in a positive manner. • Inattentive citizens who oppose voting encouragement messages or ridicule them. Chapter eight reports of the theoretical and methodological research conclusions. It also reports on practical findings and future recommendations for producers of Get Out the Vote posters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".