Powerful Partners: Ethics and Bureaucracy when Researching the Canadian Forces
Bibliographic record
Abstract
Data collection usually begins in advance of fieldwork and often continues once we leave the field. While applied anthropologists pride themselves in basing their research on authentic, in-context observations, we also conduct preliminary research on the communities we visit during our stay in the field. Furthermore, research continues back home when analyzing the data that was carefully collected. If one of the strengths of ethnographic fieldwork is in providing the opportunity to create relationships with our research subjects, it is important to remember that these relationships are not confined to our time spent in the communities. We seek contacts and permission long before arrival, often going through various forms of research approval processes. Once we leave the field, modern technology and anthropological practices ensure that we maintain these relationships. Through disseminating research results to communities, using feedback mechanisms with research participants, and continuing research interests with the same groups, the anthropologists' interests expand beyond actual fieldwork.
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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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.052 | 0.039 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".