A political economy of dentistry in Nunavut
Bibliographic record
Abstract
What factors influence the development of dcntal care in Nunavut corrmunities?Using an etlurographic câse study design (with data collected tluough participant observation, stakeholde¡ interviervs, and document reviews), this investigation proposes that fou¡ factors in.rpact on dental health care development in Nunarut: l) Geography and Disease Burden irnpact care by cornplicating the practices associated with service delivery in remote comlnunities to a population with a high prevalence of dental disease; 2) lndigenous Self-Detemination irnpacts care through the challenging socíal ¡eorientations that are necessa¡y in the context ofbuilding Canada's first Inuit 'self-govemrnent through public govemrnent;' 3) State/lndigenous Relations impact care by situating this serwice's developrnent in a series of unsettled, unclear, and politicised debates, that cornpromise effective delivery of serwice; and 4) Dental Practice and Philosophy impact care by inforrrring service delivery in a mamer that is generally ill equipped to meet the needs of such a population, and by shifting attention from public to private intelests.It is proposed that the latter two factors lequire attention if dental care in Nunavut is to meet both the health necds of individuals as well as the aspirations of Inuit for a health care systern accountable to the broad community.Ke]'r'vords: lndiger.rous
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".