Canadian natural resources large-scale projets : social, cultural and economic impacts : synthesis analysis and annotated bibliography of post-project studies
Bibliographic record
Abstract
L'equipe de recherche a voulu relever et passer en revue les etudes post-projet portant sur les impacts sociaux, economiques et culturels des projets a grande echelle relatifs aux ressources naturelles au Canada. Deuxiemement, l'equipe entendait produire une analyse de synthese des etudes trouvees. Dans le cadre du present projet, les auteurs avaient pour but final d'accroitre les connaissances sur les impacts sociaux, economiques et culturels lies aux projets a grande echelle afin d'appuyer d'une part, les conseils fondes sur des donnees probantes formules par la Division d'evaluation environnementale concernant les impacts des projets sur la sante et d'autre part, la prise de decisions de plus vaste portee ayant trait aux politiques. Les projets de ressources naturelles comprennent principalement les secteurs minier, hydroelectrique, gazier et petrolier. Selon les recherches, les projets elabores dans les secteurs comme les gaz naturels liquefies et les sables bitumineux etaient relativement recents ou a l'etape de l'obtention de l'approbation reglementaire et n'avaient pas encore commence au Canada. Les projets a grande echelle ont ete definis comme des projets reposant sur d'importants engagements financiers qui ont des repercussions mageures sur les communautes hotes.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.050 | 0.123 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".