A Population’s Perception of the Risk Management-Driven Response during Major Landslides
Bibliographic record
Abstract
Abstract The Saguenay region in Quebec has experienced several major disasters over the past few decades, namely, the Saint-Jean-Vianney landslide in 1971 and the Saguenay Flood in 1996, as well as several frequently publicized terrain instability phenomenon. In response to this reality, a unique risk management plan was implemented in Saguenay starting in 2004. The experience gained from dozens of emergency calls received in June 2022 after a widely publicized major landslide in sensitive clays in La Baie borough, along with the support provided to the population during stabilization works, highlights the impact of such large-scale events on the collective interpretation of landslide risk. This study documents the perceptions, knowledge, concerns, and opinions of a portion of the population impacted, directly or indirectly, by the urban landslide that occurred in 2022 following a rainy sping and rapid melting of the snow cover. The article presents the results of a quantitative study conducted in January 2025. A questionnaire, consisting of 74 questions designed by the co-authors of this article, was used to evaluate the evolution of various factors. These factors were assessed during significant weather events, the implementation of emergency measures in 2022, and after the occurrence of the landslide and subsequent stabilization works. The findings demonstrate the impact of incorporating individual considerations into interventions and decision-making during emergency situations. The study results also indicate that communication between citizens and responders significantly influences their perception of risks. Furthermore, this study provides details on the confidence levels of citizens regarding the safety measures implemented and their understanding of these measures. The findings offer valuable insights that can help guide institutional decisions aimed at protecting the population, emphasizing the importance of individuals’ perceptions within the existing management plan in the Saguenay region.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".