Bibliographic record
Abstract
This revised address for the 2019 Weaver-Tremblay Award revisits some underlying questions about the practice of anthropology that have figured in my own work. First, why might one choose anthropology as a means of intellectual and practical inquiry into social and cultural phenomena? Second, what kinds of anthropological practice can be pursued? Finally, what types of knowledge can be acquired through anthropological approaches, and to what purposes might this knowledge be applied? These questions are considered within the context of two rather different fields of anthropological inquiry I have pursued: relations between Indigenous Peoples and state governments, on the one hand, and the social construction of sport, on the other. As well as sharing some unexpected analytical commonalities, these ostensibly disparate fields speak to the power that resides in illuminating details of the type that anthropologists are particularly adept in recognizing.
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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 | 0.083 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.024 | 0.168 |
| Scholarly communication | 0.033 | 0.041 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.019 | 0.037 |
| Insufficient payload (model declined to judge) | 0.007 | 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".