Modelling People’s Perceptions of Environmental Issues Within GIS A Case Study Using Opinion Polls in the County of Portneuf, Canada
Bibliographic record
Abstract
Abstract: Progress in integrating GIS, multiple criteria decision-making methods and planning support systems are promising and could help reduce information gap and avoid potential landuse disputes. However, land planning has gradually moved into the public arena where various lobby groups promote their points of view. Fair and legitimate environmental risk management involves a balanced proportioning between considerations, on the one hand, of the physical impact on natural ecosystems and, on the other, psycho-social effects which affect the perception of, as well as any reaction to, disturbances in the surroundings. This paper develops a methodology for assessing perceived risks using a procedure which is compatible with indicators of the state of the physical environment. It uses GIS technology to detect conflictprone areas. In order to assess the relative importance of perceptual factors, this text combines a public opinion poll conducted among a sampling of the Portneuf population with a GIS presenting the distribution of features and activities across the region. Statistical analysis synthesised the information and made it possible to build behavioural models. It was carried out in three stages: contingency tests, spatial autocorrelation assessment, and discriminant analysis of respondents ’ opinions. Models yield typical profiles distinguishing the factors
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".