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Record W4387522959 · doi:10.1088/2752-664x/ad0241

Ensuring continuity and impact in Arctic monitoring: a solution-orientated model for community-based environmental research

2023· article· en· W4387522959 on OpenAlexaffabout
Louise Mercer, Dustin Whalen, Deva-Lynn Pokiak, Michael Lim, P. J. Mann

Bibliographic record

VenueEnvironmental Research Ecology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
FundersNatural Environment Research Council
KeywordsIndigenousArcticEnvironmental resource managementParticipatory action researchWorkflowBaseline (sea)Environmental planningProcess managementCorporate governanceCommunity engagementThe arcticSettlement (finance)BusinessComputer scienceManagement sciencePolitical scienceEngineeringEnvironmental scienceSociologyPublic relationsEcology

Abstract

fetched live from OpenAlex

Abstract Community-based monitoring (CBM) is increasingly cited as a means of collecting valuable baseline data that can contribute to our understanding of environmental change whilst supporting Indigenous governance and self-determination in research. However, current environmental CBM models have specific limitations that impact program effectiveness and the progression of research stages beyond data collection. Here, we highlight key aspects that limit the progression of Arctic CBM programs which include funding constraints, organisational structures, and operational processes. Exemplars from collaborative environmental research conducted in the acutely climate change impacted Hamlet of Tuktoyaktuk, Inuvialuit Settlement Region (ISR), Canada, are used to identify co-developed solutions to address these challenges. These learnings from experience-based collaborations feed into a new solution-orientated model of environmental community-based research (CBR) that emphasises continuity between and community ownership in all research stages to enable a more complete research workflow. Clear recommendations are provided to develop a more coherent approach to achieving this model, which can be adapted to guide the development of successful environmental CBR programs in different research and place-based contexts.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.257
GPT teacher head0.511
Teacher spread0.254 · 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 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

Citations10
Published2023
Admission routes2
Has abstractyes

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