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Record W2903196035 · doi:10.1080/11956860.2018.1542783

OHMi-Nunavik: a multi-thematic and cross-cultural research program studying the cumulative effects of climate and socio-economic changes on Inuit communities

2018· article· en· W2903196035 on OpenAlexafffundvenueabout
Sylvie Blangy, Monique Bernier, Najat Bhiry, Dedieu Jean-Pierre, Cécile Aenishaenslin, Suzanne Bastian, Laine Chanteloup, Véronique Coxam, Armelle Decaulne, José Gérin-Lajoie, Stéphane Gibout, Didier Haillot, Émilie Hébert-Houle, Thora Martina Herrmann, Fabienne Joliet, Annie Lamalice, Esther Lévesque, André Ravel, Daniel R. Rousse

Bibliographic record

VenueEcoscience · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsÉcole de Technologie SupérieureUniversité LavalUniversité de MontréalUniversité du Québec à Trois-RivièresInstitut National de la Recherche ScientifiqueCenter for Northern Studies
FundersAboriginal Affairs and Northern Development CanadaInstitut Polaire Français Paul Emile VictorCentre National de la Recherche ScientifiquePolar Knowledge CanadaNatural Sciences and Engineering Research Council of CanadaFonds de recherche du QuébecUniversité du Québec à MontréalArcticNetSocial Sciences and Humanities Research Council of CanadaUniversité de MontréalUniversité Laval
KeywordsPhotovoiceParticipatory action researchVulnerability (computing)GeographyEnvironmental planningCitizen journalismArcticAgricultureWildlifeEnvironmental resource managementPolitical scienceEnvironmental protectionSociologyEcologyEconomic growth

Abstract

fetched live from OpenAlex

<p>L’adaptation aux changements climatiques et socio-environnementaux est devenue un enjeu majeur pour de nombreuses sociétés, notamment dans l’Arctique. De nombreux Inuit souhaitent mieux comprendre les changements qui se produisent. En 2013, un Observatoire international sur les interactions Homme-Milieu (OHMi) a été mis en place au Nunavik pour identifier ces changements, étudier leur impact cumulatif sur le socio-écosystème et pour aider à élaborer des mesures d’adaptation afin d’améliorer le bien-être des communautés inuit. À cette fin, une équipe d’universitaires et de partenaires Inuit locaux ont uni leurs forces pour développer un programme de recherche concerté, intégré et interdisciplinaire. En utilisant une approche de recherche-action participative, l’OHMi Nunavik a défini les priorités de recherche suivantes: transmission des connaissances entre les aînés et les jeunes, agriculture nordique, préservation de la culture, de la langue et de l’identité inuit, aires protégées, emplois dans les mines, risques naturels et vulnérabilité de la faune. En renforçant les collaborations entre les équipes de recherche multidisciplinaires canadiennes et françaises, le programme de l’OHMi Nunavik intègre les connaissances locales et scientifiques dans la planification de la recherche et la diffusion des résultats.</p><h2>Abstract</h2><p> Adjusting to global climate and socio-environmental changes has become a major issue for many societies, especially in the Arctic. Many Inuit wish to better understand the changes taking place. In 2013, an international Observatory of Human–Environment Interactions (OHMi) was established in Nunavik to identify these changes, study their cumulative impact on the socio-ecosystem and to help develop adaptation measures to improve the well-being of Inuit communities. To this end, a team of academics and local Inuit partners joined forces to develop an integrated, interdisciplinary, collaborative research program. Using a participatory action research (PAR) approach, the OHMi Nunavik set the following research priorities: elder-youth knowledge transmission, northern agriculture, preservation of Inuit culture, language and identity, protected areas, mining employment, natural hazards and risks, and wildlife vulnerability. By strengthening the collaborations between multidisciplinary Canadian and French research teams, the OHMi Nunavik program integrates local and scientific knowledge both in planning the research and in disseminating the results.</p>

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.002
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.212
GPT teacher head0.532
Teacher spread0.320 · 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

Citations8
Published2018
Admission routes4
Has abstractyes

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