Bibliographic record
Abstract
This article offers a vision of how scholars in community-engaged research can use peer co-writing to rehearse ethical engagement as an ongoing, contingent practice woven throughout the research process. The authors position ethics as both a process of constant rehearsal and the thing they are rehearsing for. As the co-authors cared for each other as scholars and people, through both honest critique and humble support, co-writing became a rehearsal space for translating theoretical ideas of ethics and community engagement into embodied practice. They found María Puig de la Bellacasa’s formulation of knowledge products as “matters of care” influential in shaping how they understand and practise ethics. Positioning the enactment of care as the ethical standard of collaboration does the work of making research more sustainable and generative by placing value on the interpersonal relationships that make research viable and not only on the research product. It also allows them to interrogate how individual knowledges are informed by larger communities and challenges them to acknowledge how all thinking is a collaborative process.
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 | 0.050 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.083 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| 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".