Dissenting in reflective conversations: Critical components of doing action research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it