Developing an ethical rationale for collaborative approaches to evaluation
Bibliographic record
Abstract
As a deeply relational, dialogic, engaged and political approach, the collaborative research context is fairly unique in the world of research, and as such opens up an entirely new set of ethical considerations that serve to differentiate it from other approaches, repositioning ethics as a fundamental rationale for collaborative inquiry. In this paper, we revisit the justifications for collaborative approaches to evaluation—the three Ps—which have become integral to our discourse about the genre. We then elaborate on our rationale for exploring ethics as a legitimate interest in collaborative approaches to evaluation, with special consideration given to why ethics should become an essential consideration moving forward, specifically in terms of the moral obligations of collaborative approaches to evaluation practitioners. We then re-envision the inclusion of an “ethic of engagement” along seven interconnected dimensions, what we refer to as the Seven Rs of collaborative practice: reflexivity, relationality, responsibility, recognition, representation, reciprocity, and rights.
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 | 0.290 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.089 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.022 | 0.023 |
| 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; 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".