Bibliographic record
Abstract
Acknowledging and navigating “mess” are clear priorities in the mixed methods literature. Mess enters the mixed methods process in two interrelated ways. The first is empirically, where quantitative and qualitative findings diverge or contrast rather than cohere; the second is through design, where research contexts demand unplanned adaptation. This article outlines three practices that help mixed methods researchers recognize and navigate both kinds of mess, collectively called the “craft attitude.” The craft attitude consists of comfort with uncertainty, a nonlinear/recursive approach to research, and understanding research as storytelling. I further argue these components can orient researchers to mess in both structured and flexible ways by fostering three intellectual activities: science, craft/art, and ethical value judgment. This article contributes to the field of mixed methods by offering a practice-oriented concept facilitating the collective acknowledgement and engagement of mess, rather than concealing it in our research.
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 | 0.542 | 0.529 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.013 | 0.090 |
| Scholarly communication | 0.026 | 0.034 |
| Open science | 0.006 | 0.035 |
| Research integrity | 0.009 | 0.016 |
| 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; 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".