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Record W7070804933

Resource Extraction and Aboriginal Communities in Northern Canada Gender Consideration

2014· other· en· W7070804933 on OpenAlexaboutno aff

Bibliographic record

VenueuO Research (University of Ottawa) · 2014
Typeother
Languageen
FieldComputer Science
TopicQR Code Applications and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsConstitutionResource (disambiguation)Section (typography)Term (time)Natural resource
DOInot available

Abstract

fetched live from OpenAlex

OAAPH [known as the National Aboriginal Health Organization (NAHO)] receives funding from Health Canada to assist it to undertake knowledge-based activities, including education, research and dissemination of information to promote health issues affecting Aboriginal Peoples.However, the contents and conclusions of this report are solely that of the authors and are not attributable, in whole or in part, to Health Canada.The National Aboriginal Health Organization, an Aboriginal-designed and -controlled body, will influence and advance the health and well-being of Aboriginal Peoples by carrying out knowledge-based strategies.This report should be cited as: National Aboriginal Health Organization.(2008).Resource Extraction and Aboriginal Communities in Northern Canada: Gender Considerations.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.090
Threshold uncertainty score0.652

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0270.006
Scholarly communication0.0070.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.039
GPT teacher head0.285
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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