The network of actors and its social representations: Method of emergency and risk management evaluation in Saint-André de Kamouraska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we evaluate emergency and risk management by combining social network analysis and the study of social representations. We use a classical, bipartite network analysis method to highlight the key actors in emergency and risk management. The use of social representations anchors our data in a particular territorial experience. Indeed, the proposed article is a case study of the municipality of Saint-André-de-Kamouraska located in the Bas-Saint-Laurent administrative region of Quebec. We argue that the main advantages of our method are:
 
 to reveal the key actors in emergency and risk management;
 to reveal the impact of these actors on the governance of emergencies and risks;
 to draw the socialization to risk and emergency of the studied population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it