Dissenting in reflective conversations: Critical components of doing action research
Bibliographic record
Abstract
Reflective monitoring of research practices is essential. However, we often lack formal training in the practices of doing action research, and descriptions of actual inquiry practice are seldom included in publications. Our aim is to provide a glimpse of self-reflective practices based on our experience of enacting action research. Our engagement in a large action research project brought about many dilemmas and uncertainties related to our roles and interventions. We focus on these uncertainties as a way of opening the ‘black box’ of action research ‘in the making’. We conceptualize a methodological reflective approach that provides space for taking seriously uncertainties experienced in the field as these can be a catalyst for learning and sharpening our theoretical and empirical skills as action researchers. Through first-person inquiry, we investigate how our reflective conversations gradually evolved into second-person inquiry. We argue that enacting second-person reflective conversations renders alternative strategies for handling uncertainties through articulation of the tacit assumptions within particular empirical situations. Finally, we argue that reflective conversations should be understood and handled as iterative, open, and inquiring processes within a trust relationship, and should not be limited to any particular value schemes of action 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 | 0.279 | 0.402 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.022 | 0.099 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.012 | 0.017 |
| 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; 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".