Rendering Inuit cancer “visible”: Geography, pathology, and nosology in Arctic cancer research
Bibliographic record
Abstract
In August of 1977, Australian pathologist David W. Buntine delivered a presentation at the Annual Meeting of the Royal College of Pathologists of Australia in Melbourne, Victoria. In this presentation, he used the diagnostic category of "Eskimoma," to describe a unique set of salivary gland tumors he had observed over the past five years within Winnipeg's Health Sciences Center. Only found amongst Inuit patients, these tumors were said to have unique histological, clinical, and epidemiological features and were unlike any other disease category that had ever been encountered before. To understand where this nosological category came from, and its long-term impact, this paper traces the historical trajectory of the "Eskimoma." In addition to discussing the methods and infrastructures that were essential to making the idea of Inuit cancer "visible," to the pathologist, the epidemiologist, and to society at large, this paper discusses how Inuit tissue samples obtained, stored, and analyzed in Winnipeg, Manitoba, came to be codified into a new, racially based disease category - one that has guided Canadian and international understandings of circumpolar cancer trends and shaped northern healthcare service delivery for the past sixty years.
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 | 0.028 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".