MétaCan
Menu
Back to cohort
Record W2562030706 · doi:10.18584/iipj.2016.7.4.5

Increased Indigenous Participation in Environmental Decision-Making: A Policy Analysis for the Improvement of Indigenous Health

2016· article· en· W2562030706 on OpenAlexafffundvenue
Kerry Black, Edward A. McBean

Bibliographic record

VenueInternational Indigenous Policy Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Guelph
KeywordsIndigenousViewpointsTraditional knowledgeParticipatory action researchCitizen journalismCommunity-based participatory researchPolitical scienceEnvironmental resource managementEnvironmental planningPublic relationsSociologyGeographyEcologyLaw

Abstract

fetched live from OpenAlex

Improving the physical environment and Indigenous participation in environmental decision-making is inherently related to the improvement of health among Indigenous Peoples. Improving the state of the physical environment necessitates increased involvement by Indigenous communities in decision-making and policy development. This involvement must integrate local traditional knowledge (TK) as an important tool in the decolonization of environmental decision-making, and a necessary step towards the improvement of Indigenous health. With a focus on the physical environment as a social determinant of Indigenous health, this article highlights the need for increased Indigenous participation in the decision-making process on environmental issues and proposes a framework to accomplish this outcome. Indigenous-centred policy frameworks should include the following five key principles: (a) the recognition of Indigenous knowledge, (b) the recognition of the inherent right to self-determination, (c) the use of an inclusive and integrative knowledge system, (d) the use of community-based participatory approaches, and (e) the use of circular and holistic viewpoints.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.386
Teacher spread0.367 · 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.

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

Citations39
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueInternational Indigenous Policy JournalSame topicIndigenous Health, Education, and RightsFrench-language works237,207