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State agencies and researchers engaging with indigenous communities on climate change adaptation planning: A systematic review

2022· review· en· W4309698132 on OpenAlexaboutno aff
Bridgette Masters‐Awatere, Patricia A. Young, Rebekah Graham

Bibliographic record

VenueMAI Journal A New Zealand Journal of Indigenous Scholarship · 2022
Typereview
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersUniversity of Waikato
KeywordsIndigenousInclusion (mineral)Climate change adaptationClimate changeTraditional knowledgeKnowledge translationPolitical scienceAdaptation (eye)Strategic planningState (computer science)GeographyEnvironmental resource managementLibrary sciencePublic administrationPublic relationsEnvironmental planningKnowledge managementSociologySocial scienceBusinessPsychologyEcologyComputer science

Abstract

fetched live from OpenAlex

This systematic review centres planning, policy and/or strategic developments and implementation of climate change adaptation with Indigenous groups in Australia, Pacific Islands, Canada and the United States. We used PRISMA protocols to search five databases. The search was organised around three core areas: Indigenous people groups, climate change strategic planning, and Indigenous knowledge and active participation. A total of 6,338 articles from five databases were identified. Records were screened by title and abstract, leaving 87 articles that were assessed by full text. A total of 22 studies were included. The He Pikinga Waiora Implementation Framework was used as a matrix to analyse included articles. While studies included Indigenous groups in their research, most did not score highly for active inclusion of Indigenous knowledge, integrated knowledge translation or systems change. In general, studies had mediocre processes of inclusion that resulted in average responses and modest influence in decision-making forums

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)0.022
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.377
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.001
Science and technology studies0.0150.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.014
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 head0.349
GPT teacher head0.448
Teacher spread0.099 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations4
Published2022
Admission routes1
Has abstractyes

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