Bibliographic record
Abstract
Research about the climate and nature emergency (CNE) often reinforces colonial patterns, including the separation of humans from the rest of nature, the privileging of western scientific knowledge, and the treatment of frontline communities as data sources rather than knowledge producers. In this viewpoint, we propose a reparative approach to CNE research that foregrounds relational, material, and epistemic repair as pathways to interrupt the reproduction of systemic harm in research. Drawing on movements for climate reparations and reparative justice in higher education, we highlight the importance of interrupting extractive dynamics common to many community research collaborations, including appropriation, tokenism, and saviourism. We also offer orienting questions for researchers to engage in a sustained inquiry about how we might reconfigure research toward repair, and consider how this orientation could guide experimentation with emerging research methods, tools, and infrastructures. We close by reflecting on generative AI as one such example, asking whether it might support reparative work when engaged not as a tool for efficiency and extraction, but as a scaffold for deepening relational accountability, reflexivity, and ongoing un/learning.
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 | 0.056 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.081 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".