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Record W2863783044 · doi:10.1177/1609406918786335

Improving Qualitative Research Findings Presentations

2018· article· en· W2863783044 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.

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsQualitative researchRelevance (law)ScholarshipQualitative analysisExpression (computer science)PsychologyQuality (philosophy)SociologyEpistemologyComputer scienceSocial sciencePolitical science

Abstract

fetched live from OpenAlex

Every year thousands of presentations of qualitative research findings are made at conferences, departmental seminars, meetings, and student defenses. Yet scant scholarship has been devoted to these presentations, their nature and relevance to qualitative research, and how they can be improved. This article addresses this important gap by positioning “research findings” presentations as a distinctive genre, part of qualitative method, and an expression of scholarly discourse. From the theoretical basis of genre theory, a number of common and damaging mistakes are found to be evident in the manner in which qualitative research findings are usually presented. These have negative implications: reducing the methodological quality of, engagement with, and overall influence of the qualitative research presented. We draw on genre theory to make recommendations for future qualitative research findings presentations to improve the rigor, influence, and impact of such presentations.

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)0.022
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.712
GPT teacher head0.704
Teacher spread0.008 · 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