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Record W4323028957 · doi:10.7202/1096502ar

Required Elements for Success and Benefits of Participation in Camps of an On-The-Land Program in the Inuvialuit Settlement Region

2023· article· en· W4323028957 on OpenAlexaffvenueabout
Mary Ollier, Audrey R. Giles, Meghan Etter, Jimmy Ruttan, Nellie Elanik, Ruth Goose, Esther Ipana

Bibliographic record

VenueÉtudes/Inuit/Studies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsNunavut Research InstituteInuvialuit Regional CorporationUniversity of Ottawa
Fundersnot available
KeywordsPhotovoiceSettlement (finance)Participatory action researchCorporationGeneral partnershipLand useIdentity (music)SociologyPublic relationsEnvironmental planningEnvironmental resource managementGeographyEconomic growthPolitical scienceBusinessEngineeringCivil engineering

Abstract

fetched live from OpenAlex

We report on a partnership between the Inuvialuit Regional Corporation (IRC) and the University of Ottawa to determine whether/how on-the-land programming offered culturally safe experiences to meet the self-identified needs of the residents of the Inuvialuit Settlement Region. This study draws upon the experiences of participants in the IRC’s land-based healing program, Project Jewel. We used postcolonial theory supported by a decolonization framework and critical Inuit studies to direct this community-based research methodology. The community advisory committee and the research advisory team co-determined semi-structured interviews, sharing circles, and photovoice as the chosen research methods for this project. Results indicate that land-based healing programs considerably enhanced cultural identity and meaningful connections to social support and Inuvialuit heritage in an “on-the-land” environment. Land-based programs may thus offer an alternative and effective healing opportunity for participants who feel uncomfortable or are not being adequately served by conventional community-based or residential treatment programs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.554

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.214
GPT teacher head0.478
Teacher spread0.264 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2023
Admission routes3
Has abstractyes

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