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Record W1666510152 · doi:10.1177/160940690200100404

Exploring Qualitatively-Derived Concepts: Inductive—Deductive Pitfalls

2002· article· en· W1666510152 on OpenAlexaff
Janice M. Morse, Carl Mitcham

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEpistemologyContext (archaeology)Qualitative researchPsychologySociologyPhilosophySocial science

Abstract

fetched live from OpenAlex

Analytic induction is a sacred tenet of qualitative inquiry. 1 Therefore, when one begins a project focusing on concept of interest (rather than allowing the concepts to emerge from the data per se), how does one maintain a valid approach? When commencing inquiry with a chosen concept or phenomena of interest, rather than with a question from the data per se about what is going on, how does one control deductive tendencies to see what one desires to see and which threaten validity? Difficulties stem from the nature of induction itself – Is analytic induction an impossible operation in qualitative research, as Popper (1963/65) suggests? In this section, we first discuss Popper's concern, followed by a discussion of two major threats that may prevent an inductive approach in qualitative research.2 The first threat is the “pink elephant paradox;? the second is the avoidance of conceptual tunnel vision or, specifically, how does the researcher decontextualize the concept of interest from the surrounding context and thereby avoid the tendency to consider all data to be pertinent to the concept of interest? As we explore each of these pitfalls, and we present methodological strategies to maintain both the integrity of the concept and the integrity of the research.

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
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement 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)0.466
metaresearch head score (Gemma)0.540
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: Methods
Teacher disagreement score0.534
Threshold uncertainty score0.659

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4660.540
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0080.008
Science and technology studies0.0120.129
Scholarly communication0.0210.038
Open science0.0130.024
Research integrity0.0080.020
Insufficient payload (model declined to judge)0.0040.002

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 head0.950
GPT teacher head0.746
Teacher spread0.204 · 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.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations120
Published2002
Admission routes1
Has abstractyes

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