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Record W3154427552 · doi:10.1111/caje.12495

Indian residential schools: Height and body mass post‐1930

2021· article· en· W3154427552 on OpenAlexaffvenueabout
Donna Feir, M. Christopher Auld

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsContext (archaeology)AnthropometryDemographyLeverage (statistics)Psychological interventionEnvironmental healthBody weightGerontologyGeographyBody mass indexPsychologySocioeconomicsMedicineSociology

Abstract

fetched live from OpenAlex

Abstract We study the effects of Canadian Indian residential schooling on two anthropometric measures of health during childhood: adult height and body weight. We use repeated cross‐sectional data from the 1991 and 2001 Aboriginal Peoples Survey and leverage detailed historical data on school closures and location to make causal inferences. We find evidence that, on average, residential schooling increases adult height and the likelihood of a healthy adult body weight for those who attended. These effects are concentrated after the 1950s, when the schools were subject to tighter health regulations and students were selected to attend residential school based partly on their need for medical care that was otherwise unavailable. Residential schooling is only one policy in Canada that had an impact on the health of status First Nations peoples, so our results must be understood in the broader social context. Taken in context, our results suggest that health interventions in later childhood can have significant impacts on adult health. We also document significant increases in height and body weight for status peoples born after the 1960s, suggesting substantial changes in diet and living conditions during this period.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.024
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.123
GPT teacher head0.239
Teacher spread0.116 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicIntergenerational and Educational Inequality StudiesFrench-language works237,207