Unstable bridges—exploring the possibilities for “in between” spaces amidst divergent narratives in environmental governance
Bibliographic record
Abstract
Abstract Social-ecological systems increasingly face polarization dynamics that challenge environmental governance. Such polarization implies the development of opposing narratives with limited interaction, each framing environmental problems and solutions in distinct ways. In this study, we analyze a case of narrative polarization around the eutrophication crises of the Mar Menor lagoon in Spain, focusing on how proposed solutions are narrated to address this complex environmental puzzle. We use a mixed-method approach that combines social network analysis and an analysis of narrative practices in interview situations, to investigate whether and how potential solutions to eutrophication in the Mar Menor can be understood as bridging spaces that create opportunities for interaction between divergent societal narratives. Our three-step analysis includes: (a) a network analysis of reports proposing solutions to identify solutions with a bridging role (i.e., those linking reports that otherwise have little overlap in the solutions proposed); (b) a thematic narrative analysis to investigate the solutions proposed by diverse actors; and (c) an analysis of narrative practices around selected bridging solutions to explore if they constitute new spaces where narratives can interact and confront positions - what we call bridging spaces. We suggest this mixed methods approach allows for the identification of potential bridging spaces to mediate polarization and outline directions for future research on both the case study specifically, and on polarization in environmental governance more generally.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".