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Record W2598322147 · doi:10.29173/cais708

Using Narrative Inquiry to Collect Research Data on Life Experiences.

2014· article· fr· W2598322147 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2014
Typearticle
Languagefr
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNarrativeNarrative inquiryContext (archaeology)SociologyQualitative researchPoint (geometry)EpistemologyHumanitiesPsychologySocial scienceGeographyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Narrative inquiry is invaluable in exploring research questions because data are generated from the point of view of the study participants’ experience, and within their context, thinking, values, and actions. It is a rich form of qualitative methodology that should be considered whenever insights into complex social and cultural issues are desirable.L'enquête narrative est incomparable dans l'exploration de questions de recherche puisque les données sont générées du point de vue de l'expérience des participants et selon leurs propres contextes, systèmes de pensées, valeurs et actions. Il s'agit d'une méthodologie qualitative riche qui devrait être prise en considération lorsque des questions complexes sociales et culturelles sont en jeu.

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.

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)0.014
metaresearch head score (Gemma)0.193
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.193
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.005
Scholarly communication0.0030.007
Open science0.0050.002
Research integrity0.0000.001
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 head0.462
GPT teacher head0.522
Teacher spread0.060 · 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