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Record W7132862633

The Cold War on Cancer: Inuit and the Canadian Epidemiological Imagination

2022· dissertation· W7132862633 on OpenAlexfundaboutno aff
Jennifer Fraser

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersNational Cancer InstituteCrown-Indigenous Relations and Northern Affairs CanadaUnited Nations Educational, Scientific and Cultural OrganizationAssociação Fundo de Incentivo à PesquisaWorld Health OrganizationUniversity of TorontoPartenariat Canadien Contre Le CancerLupina Foundation
KeywordsColonialismCivilizationDiseaseEpidemiologyCancerCervical cancerPsychological interventionBreast cancerMedical anthropology
DOInot available

Abstract

fetched live from OpenAlex

From the early twentieth century to the present day, health professionals have commented on the exceptional nature of Inuit cancer patterns. Over the past hundred years, Inuit have been thought to possess a unique distribution of disease characterized, at different points, by higher than-average rates of lung, cervical and salivary gland cancer, lower than average rates of breast cancer, and substantial and growing rates of the disease overall. This thesis explores why Inuit cancer patterns have been characterized in this way and discusses their contemporary implications for Inuit health. This project argues that past and current representations of Inuit cancer incidence evolved in tandem with geographic pathology—a branch of cancer research (and predecessor of cancer epidemiology) that posited that examining the cancer rates of colonial populations could help shed light on the disease’s underlying causes. I examine how rising scientific internationalism, increased Arctic militarization, hierarchical discourses of civilization and human development, and settler policies of protectionism, segregation, assimilation, and experimentation not only allowed geographic pathology to take hold in Canada, but also transformed Inuit Nunangat into an important site in the national fight against the disease. Over the course of six-case studies, I show how Inuit served as central nodes and excluded margins of cancer knowledge production. Canadian researchers used Inuit as resources for generating etiological hypotheses and developing new diagnostic imaging devices and screening techniques. However, most of these medical insights and interventions were exported to southern Canadian urban centres—leaving Inuit to contend with the still-unfolding aftermaths of geographic pathology and its relationship to colonial rule's historical construction, and attempted elimination, of otherness. To assess the evolution and ramifications of Arctic cancer epidemiology, this thesis adopts an interdisciplinary approach, juxtaposing archival materials with contemporary accounts centered on Indigenous social actors. Oscillating between different methods, sources, and temporalities reveals how historical events resurface within contemporary conditions. It also contributes to growing scholarly interest in how cancer is understood and managed in the context of colonial and post-colonial relations of power and allows us to reflect on how settler-colonial structures produce and continue to pervade cancer service delivery in northern settings.

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 imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.967

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.010
Science and technology studies0.0590.065
Scholarly communication0.0240.011
Open science0.0040.008
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.049
GPT teacher head0.449
Teacher spread0.400 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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