Bibliographic record
Abstract
The current article articulates how the expectation of theoretical consistency can be constraining for qualitative researchers. The author considers the origins of the tradition of theoretical consistency, and suggests that postmodern research - particularly that which focuses on social justice - might in fact be served by considering possibilities that emerge from multiple theoretical perspectives. To illustrate the application and contribution of theoretical inconsistency, three concrete examples of how these ideas have been applied within qualitative studies are discussed. By pragmatically drawing connections across theoretical differences, it is hoped that researchers will engage critically with their own theoretical commitments and assumptions, thus opening themselves up to new possibilities and to new and creative ways of coming together.
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
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 | 0.474 | 0.501 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.022 | 0.234 |
| Scholarly communication | 0.038 | 0.058 |
| Open science | 0.014 | 0.045 |
| Research integrity | 0.022 | 0.044 |
| Insufficient payload (model declined to judge) | 0.007 | 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".