The Relevance of Moral Emotional Appeals in Environmental Political Debates
Bibliographic record
Abstract
Abstract: This paper examines the relevance of moral emotional appeals in the mixed dialogue type of public debates between politicians about environmentalism, relying on the concepts of goals and emotional appeals from the informal logical tradition. This paper argues that if argumentative moves are evaluated according to the action-producing dialogue type’s collective goal which could be pressing for action on environmentalism, and politicians take on the role of emotional entrepreneurs, then moral emotional appeals by politicians could evoke moral emotions in the audience, thus encouraging them to become more environmentally friendly. The influence of moral emotions on individual sustainable choices is based on empirical research from political and environmental sciences. Résumé: Cet article examine la pertinence des appels émotionnels moraux dans les débats publics de type dialogue mixte entre politiciens sur l'environnementalisme, en s'appuyant sur les concepts d'objectifs et d'appels émotionnels issus de la tradition de la logique non formelle. Cet article soutient que si les démarches argumentatives sont évaluées en fonction de l'objectif collectif du dialogue productif d'action, qui pourrait inciter à agir en faveur de l'environnementalisme, et si les politiciens assument le rôle d'entrepreneurs émotionnels, alors les appels émotionnels moraux des politiciens pourraient susciter des émotions morales chez le public, l'incitant ainsi à devenir plus respectueux de l'environnement. L'influence des émotions morales sur les choix individuels durables est fondée sur des recherches empiriques en sciences politiques et environnementales.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".