Bibliographic record
Abstract
Dr. Oakley is a is social anthropologist who is writing up her Shastri Indo-Canadian Institute and SSHRC projects, in which she worked with Indian medical anthropologists on Indian systems of medicine. Her interests lie in social medicine, food-based preventative remedies, and the tension between tradition and transformation in scientific and medical knowledge in India and Canada. She also promotes cultural sensitivity in healthcare, and has recently worked on this with Canadian public health nurses and several Indian universities. Dr. Oakley has also worked on projects broadly involving: food meanings and nutrition; social health and welfare systems in historical perspective; rural economies in transition; aging and the life course; ethnicity and race; Ebonics, ethnobotany; Unanni or Al Nabatat Al Tebia; T'ai chi ch'uan; ethnomusicology; various forms of narrative healing, critical political economy; classical social theory; socio-linguistics and the promotion and preservation of indigenous North American and Dravidian languages; preservation and classification of rare library and archival data; anthropology of education. As well as popular introductory courses and the honours seminar, Dr. Oakley has taught graduate and undergraduate classes on topics such as health, illness and aging; race, ethnicity and nationalism; food; India; South Africa; and systems of medicine in the context of empire.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.280 | 0.147 |
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".