Compatibility, integration, and epistemology: Contemporary issues from a mixed methods research experiment
Bibliographic record
Abstract
Combining quantitative and qualitative methods in Mixed Methods Research (MMR) makes it possible to benefit from the different strengths of each method. However, achieving a successful combination is not always easy. This article discusses the use of MMR in a study of clinical intervention, detailing the challenges, some insurmountable, encountered in designing the methodology, integrating the results, and preparing for the work for publication. These challenges are contextualized by reference to the current literature on MMR. The authors conclude by discussing the evolution of MMR and call for further critical reflection on compatibility, theory, and epistemology, and the resources and skills required to use the method effectively.
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 | 0.785 | 0.721 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.021 | 0.089 |
| Scholarly communication | 0.044 | 0.046 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.013 | 0.013 |
| 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".