Staying The Blazes Home: Group Charisma,\nGovernment Messaging and Covid-19
Bibliographic record
Abstract
This case study of Nova Scotia, Canada, inspects the uptake and circulation of provincial government health messaging during the early days of the COVID-19 pandemic, organized around the motto “stay the blazes home.” Messaging and collective narratives are analyzed by way of Norbert Elias’s “established/outsider” dynamics, especially focusing on his concepts of “group charisma” and “praise and blame gossip.” Economic realities and identity practices helped make this motto recognizable as a proffered collective call to action around themes of self-restraint rather than neoliberal risk calculation. Outsiders in this collective identity formation are understood to bring more than disease to the province but “anomic infection” itself. Furthermore, the mechanisms of reopening the province are also found in a longstanding and related collective ethos toward hospitality. Finally, the problematic post-pandemic dynamics of established group identity formation are examined. Résumé: La présente étude de cas sur la Nouvelle-Écosse traite de l’adoption et de la circulation des messages du gouvernement provincial en matière de santé pendant les premiers jours de la pandémie de COVID-19, lesquels reprenaient l’expression locale ≪ Stay the blazes home ≫ [Bon sang, restez chez vous!]. L’analyse des messages et des récits collectifs a été faite au moyen de la dynamique ≪ habitant ou étranger ≫ de Norbert Elias; elle se concentre particulièrement sur ses notions de ≪ charisme de groupe ≫ et de ≪ potins de louanges et de blâmes ≫. Les réalités économiques et les pratiques identitaires ont contribué à rendre cette expression reconnaissable en tant qu’appel collectif à l’action autour du thème de l’autodiscipline plutôt que du calcul de risques néolibéral. Dans cette formation d’une identité collective, les étrangers sont considérés comme apportant plus qu’une maladie à la province, soit l’≪ infection anomique ≫ elle-même. En outre, les mécanismes de réouverture de la province font également partie d’une éthique collective de l’hospitalité qui existe depuis longtemps et qui est liée à ceux-ci. Les auteures se penchent en terminant sur la dynamique problématique de la formation, au lendemain de la pandémie, de l’identité d’un groupe établi.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".