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Methodological Precision in Qualitative Research: Slavish Adherence or “Following the Yellow Brick Road?”

2015· article· en· W294247824 on OpenAlexaff
John R. Cutcliffe, Henry G. Harder

Bibliographic record

VenueThe Qualitative Report · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPositivismEpistemologyIdealizationQualitative researchArgument (complex analysis)SociologyScientific methodUnderpinningGrounded theorySocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Qualitative research has withstood many challenges on its way to becoming a credible research paradigm, though it remains the case that the paradigm contains ongoing methodological debates. One such debate is, for want of a better expression, the necessity for methodological precision (fundamentalism or purity). While it is accurate that research methodologies are somewhat fluid in that they are refined over time, it is equally correct that some researchers fall into a trap in claiming such fluidity is the reason for their imprecise use of a research methodology. Given that scientific knowledge is inextricably linked to the practice of method (at the very least for those who subscribe to positivist, post-positivist and to some extent modernist views) and that method is prefaced and underpinned by methodology, if methodological slippage has occurred and there is resultant incongruity between methodology and method, then an argument can be made that the study is not a scientific study and consequently cannot make the claim that it has produced scientific knowledge. Even allowing for some movement from the abstract, idealization of a given methodology into the “real world” application of the method, it is essential to note that variation in or movement away from a method’s underpinning methodology and epistemological stances can and does occur in well-designed studies; but if such movement occurs purposefully and/or has an robust rationale, grounded in the method’s original 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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
gptMetaresearch
Domain: Methods · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.631
metaresearch head score (Gemma)0.767
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.369
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6310.767
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0080.011
Science and technology studies0.0180.173
Scholarly communication0.0230.040
Open science0.0080.030
Research integrity0.0200.031
Insufficient payload (model declined to judge)0.0050.002

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 head0.942
GPT teacher head0.784
Teacher spread0.158 · 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

Labeled directly by 2 models reading the full record.

Study designTheoretical or conceptual
DomainMethods
GenreEmpirical · Commentary

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

Citations20
Published2015
Admission routes1
Has abstractyes

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