Un cadre de gouvernance collaboratif dédié à l’appréciation des risques des systèmes essentiels dans un contexte de changements climatiques
Bibliographic record
Abstract

 
 The fall 2021 climate events in British Columbia raise questions about the resiliency of communities, but also of critical systems, with respect to the allocation of roads, telecommunications, drinking water, electricity, etc. The interdependence of these systems can quickly generate serious consequences for populations and socio-economic activities that local and regional authorities must manage. The diversity and number of stakeholders require these authorities to coordinate well. How then can the risks to which they are exposed be better assessed? How can we ensure coherence in the measures put in place in terms of planning, preparation and response?
 The concepts and results presented in this article are the result of an action-research project carried out with the participation of numerous partners, particularly, but not exclusively, from municipal milieus in two regions of Quebec (Argenteuil and Brome-Missisquoi MRCs). This active collaboration with these regional partners has made it possible to propose an approach for implementing a collaborative governance framework combined with a risk assessment process. The tools and mechanisms associated with this approach will allow regional authorities to better understand the impact of climate change on the territory and to ensure consistency in the risk management actions of the various stakeholders.
 
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.003 |
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".