MétaCan
Menu
Back to cohort
Record W2169767569 · doi:10.46743/2160-3715/2009.2825

Facilitating Coherence across Qualitative Research Papers

2016· article· en· W2169767569 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Qualitative Report · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsUniversity of Calgary
FundersUniversity of Illinois at Urbana-ChampaignNova Southeastern UniversityUniversity of Calgary
KeywordsCoherence (philosophical gambling strategy)Qualitative researchPresentation (obstetrics)Computer scienceEpistemologySociologyEngineering ethicsSocial scienceEngineeringPhilosophy

Abstract

fetched live from OpenAlex

Bringing the various elements of qualitative research papers into coherent textual patterns presents challenges for authors and editors alike. Although individual sections such as presentation of the problem, review of the literature, methodology, results, and discussion may each be constructed in a sound logical and structural sense, the alignment of these parts into a coherent mosaic may be lacking in many qualitative research manuscripts. In this paper, four editors of The Qualitative Report present how they collaborate with authors to facilitate improvement papers’ coherence in such areas as co-relating title, abstract, and the paper proper; coordinating the method presented with method employed; and calibrating the exuberance of implications with the essence of the findings. The editors share exercises, templates, and exemplary articles they use to help mentor authors to create coherent texts.

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)0.017
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.336
GPT teacher head0.575
Teacher spread0.239 · 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