Reimagining Inquiry: A Transformative Integration of Mixed Methods Action Research and Qualitatively Driven Designs in Disability Research Contexts
Bibliographic record
Abstract
This theoretical paper advances participatory and justice-oriented mixed methods research by integrating qualitatively driven mixed methods research with mixed methods action research within Mertens’ transformative paradigm, specifically in the context of disability studies. We critique post-positivist disability research for positioning people with disabilities as passive subjects and propose an alternative that centers lived experience and co-creation of knowledge. We position qualitatively driven mixed methods research as providing an ethical and epistemological foundation for these aims by privileging qualitative inquiry to shape the research design, while selectively incorporating quantitative tools to extend and contextualize findings without displacing the primacy of qualitative approaches. With mixed methods, action research contributes iterative, participatory cycles that translate insights into meaningful action. Together, this integration offers a coherent and ethically robust framework for addressing entrenched inequities and the “wicked problems” faced by disability communities. Philosophical synthesis of the transformative paradigm and critical realism underpins this approach, enabling both the recognition of systemic power and the explanation of its generative mechanisms. The proposed integrated, qualitatively driven mixed methods research, employing a mixed methods action research model, responds to calls for reflexive, equity-driven, and action-oriented mixed methods designs. This approach bridges conceptual innovation with practical guidance for designing rigorous, participatory, and socially impactful studies in health and social science research.
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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 | 0.288 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.087 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".