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Record W2560223254 · doi:10.1177/1468794116679726

Criteria for quality in qualitative research and use of freedom of information requests in the social sciences

2016· article· en· W2560223254 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueQualitative Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicData Analysis and Archiving
Canadian institutionsCarleton UniversityUniversity of Winnipeg
Fundersnot available
KeywordsQualitative researchQuality (philosophy)SociologyFreedom of informationQualitative propertyWork (physics)Public relationsSocial scienceEpistemologyPolitical scienceComputer scienceLawEngineering

Abstract

fetched live from OpenAlex

Access to information (ATI) and freedom of information (FOI) requests are an under-used means of producing data in the social sciences, especially across Canada and the United States. We use literature on criteria for quality in qualitative inquiry to enhance ongoing debates and developments in ATI/FOI research, and to extend literature on quality in qualitative inquiry. We do this by building on Tracy’s (2010) article on criteria for quality in qualitative inquiry, which advances meaningful terms of reference for qualitative researchers to use in improving the quality of their work; and illustrating these criteria using examples of ATI/FOI research from our own work and from others’ in Canada, the United States, and the United Kingdom. We argue that, when systematically designed and conducted, ATI/FOI research can prove extraordinary in all eight of Tracy’s criteria.

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)0.145
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1450.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.007
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.682
GPT teacher head0.705
Teacher spread0.023 · 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