Towards Community-Directed Climate Adaptation Research (C-DAR)
Bibliographic record
Abstract
The disconnection between humans and their surrounding ecologies, intensified since the industrial revolution and often described as the “metabolic rift,” has profoundly influenced dominant research practices as enterprises of knowledge production. As such, much research today, including climate change research, is disconnected from the land and the communities who depend on and steward it. This paper starts by briefly tracing a response to this disconnection, the emergence of community-engaged approaches to action research. Turning on the urgency of community adaptation under the climate emergency, we point to how this trajectory has seeded a paradigm shift to what we are referring to as community-directed research (C-DAR). Drawing on dialogues with three Indigenous community partners in Brazil—the Tremembé, Mundurukú, and Guaraní—we theorize how C-DAR could navigate four critical boundaries to bridge the rift between researchers and the land: The rift between western science and Indigenous and local knowledges; the rift between natural and social sciences; the rift between institutional (university) and community contexts; and the rift between the Global South and the Global North. We conclude with an observation that the acronym “C-DAR” is phonetically identical in Brazil to the Portuguese reflexive verb “se dar” which can mean “to give oneself to something.” In this spirit of “se dar,” we offer our paper as a call for experiments in reparative approaches to research, inviting scholars to reflect on their willingness to engage with this ethos.
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 | 0.154 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.108 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".