Préférences hétérogènes des grands projets miniers : trois essais en évaluation non marchande
Bibliographic record
Abstract
Accurate evaluation of mining impacts is highly challenging given the strong magnitude of socio-economic and environmental changes at play, and possibly related controversies. This thesis raises the question on how mining impacts well-being with a primary focus on population heterogeneity. A choice experiment survey is conducted to collect ground information on changing well-being due to mining within the province of Quebec, in Canada. Article 1 points to the importance of the geographic context, marked by spatial inequalities in mining impacts. We find that mining development can have long-range impacts on welfare, related to the type of mineral and individual risk perception. Paper 2 takes advantage of strong gold mining history in Quebec to study whether collective experience facilitates mining trade-offs over rare earths, that are new to the province and often poorly known by general public. Finally, paper 3 tests potential effects of information campaigns on welfare changes. Information appears to have little or no effect. However, this result masks high and contrasting effects according to opposing prior beliefs for or against mining windfall. The thesis concludes on relevant research extensions to help estimate mining effects on people’s well-being.
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 | 0.050 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".