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Record W4403667779 · doi:10.1016/j.ehb.2024.101442

The physical well-being of Indigenous communities in the Pacific Northwest: Anthropometric evidence from British Columbia’s jails, 1864–1913

2024· article· en· W4403667779 on OpenAlexafffundabout
Kris Inwood, Ian Keay

Bibliographic record

VenueEconomics & Human Biology · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsQueen's UniversityUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaMinnesota Population Center, University of Minnesota
KeywordsIndigenousAnthropometryGeographyDemographyGerontologyHistoryArchaeologySociologyMedicineEcology

Abstract

fetched live from OpenAlex

This paper documents the height of Indigenous men from the Pacific Northwest who were incarcerated in British Columbia's jails during a period of colonization and increasing market access. The average height of adults from a given community reflects the standard of living in that community at the time the adults were growing to maturity. After correcting for the impact of sample selection arising from prisoners' personal attributes, their home communities' access to market opportunities, and unobserved height determinants associated with exposure to the colonial criminal justice system, we find that Indigenous men were positively selected into incarceration based on their height. Moreover, the tendency for the tallest men to be incarcerated became stronger over our period of study. Our results suggest that Indigenous communities in the Pacific Northwest were at a severe bioeconomic disadvantage during the nineteenth century, and their well-being did not improve as market access and colonial institutions spread through the region.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.245
Teacher spread0.211 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2024
Admission routes3
Has abstractyes

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