‘Examining Sustainable Development Challenges and the Role of Company-Consultancy Partnering in Creating Value: The Case of the Canadian Mining Industry.’
Bibliographic record
Abstract
First, I would like to thank my supervisor Dr. Blair Feltmate. Blair’s practical approach, extensive knowledge, and positive encouragement have been instrumental to the successful completion of this Major Paper. I would also like to thank my advisor Dr. Paul Wilkinson, who demonstrated patience during the numerous revisions of my Plan of Study and Major Paper Proposal, and without whom I would not have been able to progress through the Master in Environmental Studies program as fluently as I did. Furthermore, many thanks go to all the professors and staff in the Faculty of Environmental Studies at York University. Their dedication, knowledge and passion are unrivaled. I would also like to thank the mining industry professionals and consultants who participated in my research interviews. Many invaluable insights were provided during these interviews, and their contributions facilitated a more informed and constructive product. Furthermore, I would like to thank my family and friends. I thank them for listening to my frustrations, and providing me with encouragement and hope. I would
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.044 | 0.026 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".