Bibliographic record
Abstract
Over the two decades since the initial grant application, more than one SSHRC grant was necessary to keep the project afloat.I am very grateful for that support.Thanks also to the editors of Arctic and Canadian Public Administration for allowing me to use material that first appeared in "Cultures in Collision: Traditional Knowledge and Euro-Canadian Governance Processes in Northern Land-Claim Boards, " Arctic 59 (December 2006): 401-14; "'Not the Almighty': Evaluating Aboriginal Influence in Northern Land-Claim Boards, " Arctic 61, Suppl. 1 (2008): 71-85; and "Issues of Independence in Northern Aboriginal-State Co-management Boards, " Canadian Public Administration 61 (December 2018): 550-71.Along the way a great many people proved uncommonly helpful sources of information, assistance, and advice.I owe enormous thanks to the many dozens of (current and former) board members, staff, government officials, and other knowledgeable Northerners who agreed to interviews -many more than once -follow-up calls, and e-mails as well as document requests.All were promised anonymity, and, accordingly, no attributions are made of quotations or paraphrases from the interviews.Aside from the interviews, a number of people were especially helpful in various ways; none, of course are responsible for any errors or dubious interpretations.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.564 | 0.308 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".