The reliance on conceptual frameworks in qualitative research – a way forward
Bibliographic record
Abstract
BACKGROUND: While acknowledging that theory can be critical to scientific progress, we are concerned about instances of its tendency to encroach on, or replace, deep engagement with data in qualitative research. We discuss conceptual frameworks' role in conducting and teaching qualitative research. METHODS: We address three questions about our attachment as researchers to theory through conceptual frameworks: (1) What do conceptual frameworks offer qualitative research?; (2) Why do researchers use and teach conceptual frameworks in qualitative research?; and (3) How can we practice and teach rigour while integrating conceptual frameworks in qualitative research? RESULTS: One way that theory may be misused in qualitative research is in the development and reliance on conceptual frameworks as a prescription for data collection and analysis. We suggest possible ways forward to ensure rigour while integrating frameworks in qualitative research, such as examining the evolution of our own theoretical perspectives. CONCLUSIONS: We need to impart to our students the value of thinking deeply about their own data, of knowing what came before, and of taking the time and making an effort to unite these strands into novel and interesting results.
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 | 0.848 | 0.783 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.021 | 0.199 |
| Scholarly communication | 0.052 | 0.101 |
| Open science | 0.019 | 0.046 |
| Research integrity | 0.034 | 0.071 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".