Bibliographic record
Abstract
The growing recognition of mixed methods research as a valuable approach to understanding our complex world, along with the emergence of hybrid designs, presents opportunities for prioritizing qualitative integration. Integration, the defining feature of mixed methods research, requires intentional and strategic efforts to generate novel insights by combining qualitative and quantitative approaches in various ways. In qualitatively oriented designs, integration plays a critical role in preserving and amplifying qualitative contributions, yet little guidance exists for researchers. While qualitatively oriented mixed methods research is widely recognized within the broader mixed methods field, defining its distinct niche remains underexplored. This paper, based on a keynote presented in the online component of the 9th World Conference on Qualitative Research, sheds light on the often overlooked potential of mixed methods designs that prioritize qualitative perspectives in their integration with quantitative research approaches. Central to our exploration is the fundamental question: What guides the design of qualitatively oriented mixed methods research? To that end, we examine the application of key mixed methods practices to help researchers bring integration to the forefront of their qualitatively oriented mixed methods designs. Using compelling examples and describing practical strategies, we provide guidance on leveraging qualitatively oriented mixed methods research as a distinct and valuable approach— enhancing methodological rigour and integration evidence. Researchers benefit from practical guidance, ensuring that the unique strengths of qualitative inquiry are both elevated and enhanced in mixed methods designs. Together, we unlock the potential of qualitatively oriented mixed methods to navigate and understand our intricately woven world.
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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 | 0.535 | 0.494 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.033 | 0.029 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".