Different Paradigm Conceptions and Their Implications for Qualitative Research
Bibliographic record
Abstract
Paradigms have been often presented as fundamental to how we should conceive of and conduct qualitative research. Some writers even hold that defining a researcher’s own paradigm, i.e., including defining their own ontology and epistemology, should be the starting point for conducting any qualitative project. Yet there appears to be little recognition of the uniqueness of the researcher-defined paradigm model often promoted within qualitative research or the existence of alternative paradigm conceptions. Based on an analysis of the original texts, I compare the researcher-defined paradigms proposed by Guba and Lincoln with paradigm conceptions proposed by Kuhn (1970) and Burrell and Morgan (1979), highlighting fundamental differences in their rationale, definition, who or what has a paradigm, how they arise, the positions that researchers can adopt, the scope of their ontological claims, their relation to specific research projects, examples of paradigm positions, and their tenets. The analysis shows that while the three sets of authors all refer to their constructions as paradigms, they present distinct, unrelated paradigm models. Recognizing the potential of distinct paradigm conceptions opens a space for qualitative researchers to reexamine their own commitments. Given the potential alternatives, qualitative researchers who continue to appeal to researcher-defined paradigms at the very least should be able to justify both their choice of paradigm conception and the position they have chosen within it. That there are viable alternatives should allow qualitative researchers to reconsider whether the researcher-defined paradigm model remains the best approach for presenting their assumptions related to a project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.443 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.019 | 0.118 |
| Scholarly communication | 0.034 | 0.058 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".