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Record W2131398129 · doi:10.21083/surg.v4i2.1184

Globalisation, collaboration, and indigenous alcohol abuse

2011· article· en· W2131398129 on OpenAlexvenueaboutno aff
Thomas William Piggott

Bibliographic record

VenueSURG Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersPublic Health Agency
KeywordsIndigenousAlcohol abusePublic healthEthnic groupGlobalizationPolitical scienceGovernment (linguistics)Environmental healthMedicineCriminologyPsychiatryPsychologyNursingLaw

Abstract

fetched live from OpenAlex

Alcohol abuse is attributed to four percent of the global burden of disease and associated with over 60 medical conditions. This burden is borne disproportionately by the indigenous peoples of our world. Two such indigenous populations, albeit far from one another, who are suffering from alcohol abuse are the San in Botswana and the First Nations in Canada. Both marginalised populations have high rates of alcohol abuse; however, there is a clear need for more research into the epidemiology. The public health response to alcohol abuse in indigenous populations is at a different stage in Canada and Botswana. In Canada, alcohol abuse among the First Nations has been on the agenda of public health since the release of the Indian Relations Paper in 1975. In Botswana, alcohol abuse among the San has yet to be recognized– the government response has been blind to ethnicity. This paper examines the similarities and differences between alcohol abuse issues, providing evidence that increased collaboration would lead to benefits for both populations. Neither side, those responsible for the public health of the San or First Nations, has an impeccable record – both sides could learn much from the successes and failures of the other, and other indigenous populations suffering from alcohol abuse globally.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.286
Teacher spread0.244 · 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 teacher head, 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

Citations0
Published2011
Admission routes2
Has abstractyes

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