“I haven’t seen any results yet”: on ethical collaborative research in linguistics and the need for a standard protocol
Bibliographic record
Abstract
Abstract What does ‘good’ ethical conduct in linguistics look like from the perspective of the communities with whom we work? We address this question by drawing on observations from a community-bridging and knowledge exchange exercise involving both researchers and members of researched communities. Based on the experiences of those co-authors on the research team working with and for two associations representing Latin Americans and Chagossians in the United Kingdom, we discuss power asymmetries in collaboration, academic gatekeeping, and issues surrounding knowledge production, with specific reference to the design and implementation of funded research projects in formal linguistics. The paper’s originality lies in the practical recommendations made to the formal linguistics community on the basis of our synthesis of the testimony offered, so as to promote equitable and ethical research conduct. These include a prioritising of principles drawn from community-based and participatory research frameworks, the co-design of a long-term action plan, and a reconsideration of resource allocation to incorporate opportunities for professional development and infrastructure building. Our contention is that striving for a research design that is beneficial to the community on their terms should be the guiding star of project planning inasmuch as it is both ethically compelling and achievable.
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 | 0.801 | 0.819 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.018 | 0.042 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".