Beyond Technical Fixes: climate solutions and the great derangement
Bibliographic record
Abstract
Climate change research is at an impasse. The transformation of economies and everyday practices is more urgent, and yet appears ever more daunting as attempts at behaviour change, regulations, and global agreements confront material and social-political infrastructures that support the status quo. Effective action requires new ways of conceptualizing society, climate and environment and yet current research struggles to break free of established categories. In response, this contribution revisits important insights from the social sciences and humanities on the co-production of political economies, cultures, societies and biophysical relations and shows the possibilities for ontological pluralism to open up for new imaginations. Its intention is to help generate a different framing of socionatural change that goes beyond the current science-policy-behavioural change pathway. It puts forward several moments of inadvertent concealment in contemporary debates that stem directly from the way issues are framed and imagined in contemporary discourses. By placing values, normative commitments, and experiential and plural ways of knowing from around the world at the centre of climate knowledge, we confront climate change with contested politics and the everyday foundations of action rather than just data.
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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.017 | 0.039 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".