Doing Research ‘With’: Reading and writing our difference
Bibliographic record
Abstract
As two women engaged in different research domains within the broader milieu of adult education, we perform critical action research on our own research practices of reading and writing research. This article is our dialogical narrative of reading and responding to each other's research and co-writing this process with a view to improving our practice of understanding the other and augmenting the attendant theory. It is our position that dialogical inquiry constitutes reflective practice and offers us multiple speaking positions to enact moral agency and social change through the narrative mode. We present a dialogical mode of engagement for action research that is based on an ethical mutual relationship. We speak in dual voices to embody our particular practices of critically reading each other's research. We make explicit this morally and epistemically valuable process of 'reading each other', surfacing tacit assumptions, personal preferences and orientations, similarities and differences. We generate a methodological and epistemological approach to improving the practices of reading and responding in writing to the other's research and in co-writing research. It is our position that this approach of relational knowing contextualizes transformative action research and authorship at the intersection of 'I' and 'you', self and other, theory and practice, public and private, reader and writer and text.
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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 | 0.033 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.069 |
| Scholarly communication | 0.030 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".