Arts and Mixed Methods Research: An Innovative Methodological Merger
Bibliographic record
Abstract
Integrating the arts with mixed methods research (MMR) presents untapped potential for innovative methodological approaches. Arts and MMR integration exists on a continuum, ranging from low-level (e.g., communicating about MMR using art) to high-level integration (e.g., interweaving arts-based and MMR approaches), and myriad art forms are available to facilitate concept formation, data collection, analysis, and representation. Given that a primary objective of the arts and MMR respectively is to explore and understand the complex social world, arts–MMR integration has potential to enable insights not possible through the use of either approach in isolation, and to present new opportunities for transformative social change. In this article, we explore such potentials and intersections philosophically and methodologically by way of four case examples framed by the newly conceptualized Art-MMR Integration Continuum, which ranges from communicative, data source, analytic, and conceptual integration.
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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 | 0.298 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.010 | 0.095 |
| Scholarly communication | 0.036 | 0.032 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".