MétaCan
Menu
Back to cohort
Record W2998231867 · doi:10.1177/1609406919894796

<i>Aajiiqatigiingniq</i> : An Inuit Consensus Methodology in Qualitative Health Research

2019· article· en· W2998231867 on OpenAlexafffundabout
Priscilla Ferrazzi, Shirley Tagalik, Peter Christie, Joe Karetak, Kukik Baker, Louis Angalik

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMakivik CorporationUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsIndigenousNarrativeTraditional knowledgeQualitative researchSociologyPsychologyPublic relationsPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Indigenous knowledge and approaches to health research have historically been marginalized by Western traditions. Efforts to overcome this marginalization by recognizing Indigenous methodologies as a distinctive form of inquiry are gathering momentum. Health research that seeks to establish levels of agreement about disputed or conceptually unclear subjects frequently relies on consensus methods. Aajiiqatigiingniq is a principle of cultural knowledge and a consensus decision-making approach among Inuit in the Canadian Arctic. We used group meetings and individual interviews involving Inuit elders and other senior community members in Arviat, Nunavut, to explore and describe aajiiqatigiingniq as an appropriate and ethical methodology in qualitative health research. Findings reveal a systematic but apparently informal approach focused on sustained individual and community well-being. Consensus is achieved through the successive addition of group members, respectful communication, mainly narrative discourse, subjective personal engagement, and an unhurried meeting style. While previous research has used Western consensus methods to embed Inuit knowledge in health research, this study provides a first descriptive account of a wholly Inuit consensus methodology.

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)0.198
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1980.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.932
GPT teacher head0.804
Teacher spread0.129 · 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; both teacher heads agree on what is shown here.

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

Citations30
Published2019
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Qualitative MethodsSame topicIndigenous Studies and EcologyFrench-language works237,207