Exploring Qualitatively-Derived Concepts: Inductive—Deductive Pitfalls
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.
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it