Bibliographic record
Abstract
In this paper I explore societal polarization as a challenge to environmental governance and sustainability transformations. I focus on how processes of knowledge co-production can be used as transformative avenues in highly polarized environmental disputes. I investigate the case of nonpoint driven eutrophication in the Mar Menor Lagoon, Spain, where the interplay of divergent epistemic, political, and affective processes exacerbates societal divisions around environmental degradation. Drawing on process-relational and affect theory, I argue that co-production within polarized contexts might focus on relational transformation by placing attention on how differences emerge from and are transformed within affective relations in knowledge encounters. Through a diffractive reading of a co-production experience with actors holding polarized positions in relation to the Mar Menor, the paper sheds light on the affective patterns underpinning polarization by framing, blaming, and eluding responsibility, which is termed the “responsibility trap.” It suggests that transcending “us vs them” dichotomies in environmental disputes calls for an affective engagement that shifts the responsibility trap to matters of co-response-ability. In our co-production experience, a partial relational transformation in this direction was achieved recognizing the lagoon as a shared matter of care, foregrounding how knowledge affects, embodying polarized narratives, exposing uncertainties in contested facts, and demonstrating that action can be taken even under uncertain conditions. Such a relational shift enabled a preliminary weaving of ways of knowing-feeling-becoming with the social-ecological transformation of the Mar Menor.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".