Bibliographic record
Abstract
Ben Bradshaw is an Associate Professor in Geography. His main strand of research focuses on relations between Aboriginal communities and mining firms in Canada, and especially their use of negotiated agreements – typically called Impact and Benefit Agreements (IBAs) - to settle their differences. This research has been aggressively oriented towards the needs of IBA signatories, which has been achieved, in part, through the creation of the popular IBA research network. Related work has sought to assist communities to develop a meaningful but systematic means of tracking change in their well-being in light of mining. For more information about Ben Bradshaw’s research, please go to his website at https://www.uoguelph.ca/geography/people/faculty/bradshaw.shtml \nNoella Gray is an Assistant Professor in Geography. Broadly, she is interested in the politics of conservation and environmental governance – in how access to natural resources is defined, contested and legitimated by resource users, experts, civil society and the state. More specifically, she considers how science is incorporated into environmental policy, the politics of scale in marine conservation, and how resource management policies are negotiated under co-management arrangements. For more information about Noella Gray’s research, please go to her website at https://www.uoguelph.ca/geography/people/faculty/gray.shtml
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.328 | 0.121 |
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".