Positioning blue justice at local scales: insights for transdisciplinarity through art-science integration
Bibliographic record
Abstract
Since its introduction in 2018, the term blue justice has gained considerable traction. However, significant gaps and inconsistencies in the emerging literature remain. To address these issues, we have developed a collective framework that aims to contribute to and expand transdisciplinary blue justice research. As part of this framework, a transformative and participatory research design has been co-produced and applied in the Gulf of Arauco in the center-south of Chile. The results of our research suggest that the integration of scientific and artistic methods stimulates social-ecologically engaged transdisciplinary research centered on the identification of (1) root causes of social-ecological injustices that coastal communities face on a daily basis; (2) resistances in the face of these injustices, including forms of collective action and specific vocabulary that gives voice to marginalized coastal peoples; (3) opportunities that help to envision alternative coastal futures and pathways for blue justice, such as memory, emotions, local knowledge, and the strengthening of social-ecological identities. By transcending disciplinary boundaries, we envision blue justice as providing a suitable analytical lens through which to trial, apply, and evolve much-needed transdisciplinary research theory and praxis in coastal areas, emphasizing fairness, inclusivity, and the right of small-scale fishers to progressively exercise sovereignty over their territory through inclusive coastal governance.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.073 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".