Bibliographic record
Abstract
The idea for this book arose from a rather heated conversation between my partner, a non-Native hunter, and my colleague, an anthropologist who studies First Nations and sympathizes with their quest for recognition of their Aboriginal rights and title.She was arguing in support of their rights to hunt (and fish), while he was insistent that he too should have the same rights to hunt because hunting was just as integral to his identity as it was to those of the First Nations.As someone who felt that discretion was the better part of valour at this point, I sat on the sidelines listening and realized that this debate had been going on a long time and was still being discussed not only among individuals but also within government, public organizations and lobbies, and the courts.It therefore seemed to me that hunting was a pivotal issue within Canada, not only currently but historically as well.It also seemed to me that academics, especially within the social sciences and humanities, were paying too little attention to hunting as a field of study and that, if the debates (there are more than the one noted above) over hunting were ever to be understood, if not resolved, then research would have to be done, and hunters, whether Aboriginal or not, would have to have their say.Hence this book and its attempts to bring the issues surrounding hunting and its importance to Canadian history and society to a broader, more public, audience.In putting this collection of articles together, I must first and foremost acknowledge the help and support of all the contributing authors.It has been a long and sometimes arduous process, with all the contributors being very busy individuals.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.415 | 0.215 |
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".