Effective or Not? Building Discussions on Dilemmas and Refusals Into Research Practices: Perspectives From Participants
Bibliographic record
Abstract
Within the academy, pedagogical practice includes how we teach about, design and conduct research. Too often, research has been carried out, in extractive ways, “on” Black, Indigenous, racialized and marginalized communities, rather than alongside them. Much of the research conducted in these communities centers on damage (Tuck, 2009) and operates on a faulty theory of change that imagines policymakers will adjust their systems just because scholars report on damage (Tuck & Yang, 2014). Using results from my dissertation field research, which built questions about the research process itself into my 32 qualitative interviews, this paper presents the perspectives of community members living and working along the Thailand-Myanmar border. It spotlights the usefulness of research and its potential to make change. I propose that to approach research in a more ethical and transformative way, academic researchers must include questions about the research itself into their fieldwork. Also, researchers should approach the work with healthy skepticism about why they are doing it at all, whether it’s already been done, and whether it could be done better by someone else. Participants offered concrete steps for how to make research more useful, involving as many community members/organizations as possible from the outset. They discussed only doing research based on long-term trusting relationships formed with communities; understanding cultural context; making consent forms easy to read, culturally appropriate and clear; sharing and collaborating on results with communities; making sure that publications and writing would be accessible; and using the research as a tool for advocacy or political activism outside the academy.
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 | 0.172 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.035 | 0.068 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.018 | 0.033 |
| 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; 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".