Losing Heart: Declining Support and the Political Marketing of the Afghanistan Mission
Bibliographic record
Abstract
Abstract.Public opinion shifted markedly between 2006 and 2007 regarding Canadian military participation in Afghanistan. Multivariate analysis of survey data reveals that the interplay of cognitive and emotional responses fractured support and consolidated opposition to the mission. Subsequently, a major government communication strategy, aimed at bolstering support for the Afghan mission succeeded at an informational level but failed to connect at an emotional one, leaving overall support for the mission essentially unchanged. Our analysis points to the need for nuanced interpretation of shifts in public support for war as well as in assessing political marketing efforts by government. Résumé.L'opinion publique s'est nettement décalée entre 2006 et 2007 concernant la participation militaire canadienne en Afghanistan. L'analyse multi variée des données d'aperçu indique que l'effet des réponses cognitives et émotives a divisé l'appui et a consolidé l'opposition à la mission. D'ailleurs, une stratégie importante de communication du gouvernement, destinée à augmenter le soutien de la mission afghane a réussi à un niveau informationnel, mais ne s'est pas reliée au niveau émotif, laissant le soutien global de la mission essentiellement inchangé. Notre analyse indique le besoin d'une interprétation diversifiée et nuancée des variations de soutien public face à la guerre ainsi qu'une évaluation du marketing politique du gouvernement.
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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 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".