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Record W2729778696 · doi:10.22584/nr45.2017.008

Body Image Dissatisfaction (BID) from an Indigenous Alaska Native Female Perspective (A Pilot Study)

2017· article· en· W2729778696 on OpenAlexvenueno aff
Karaline Mae Naegele, Christine Cook

Bibliographic record

VenueThe Northern Review · 2017
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPerspective (graphical)GeographyPsychologyEcologyBiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The Northern Review 45 (2017): 141–160https://doi.org/10.22584/nr45.2017.008The study was conducted as a preliminary investigation regarding body image dissatisfaction (BID) in Indigenous females living in Alaska. As BID has been a notable area of concern for European American females, and a growing concern for several other cultural groups in North America, it is important to determine whether BID is a concern for the Alaska Native population. The research was comprised of qualitative interviewing methods. Interviews were conducted with Alaska Native female participants between the ages of 18 and 23 years who were attending the University of Alaska Fairbanks. Research questions addressed whether or not Indigenous Alaska Native females experience BID, and if so how BID develops and manifests for this population. The study found that all participants experienced BID as young adults. The manifestation of BID varied on an individual basis, as seen in other research findings. Participants provided suggestions for working with Indigenous Alaskan females in regards to BID.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.401
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2017
Admission routes1
Has abstractyes

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