Blue Justice and the co-production of hermeneutical resources for small-scale fisheries
Bibliographic record
Abstract
Blue Justice emerges as a counternarrative to the promise and commitment to Blue Economy and Blue Growth by shifting imperatives for growth and innovation to the central role played by small-scale fisheries and social justice in sustainable ocean development. To instrument Blue Justice, it is important to understand injustices experienced by small-scale fisheries people which can range from accusations of disregard for the environment to equating their fishing practices as illegal, or even the sudden usurpation of their customary fishing grounds and means of livelihoods. Drawing on Fricker’s concept of epistemic injustice, we examine how discrimination and lack of interpretative concepts to communicate unjust experiences wrongs small-scale fisheries people in their capacity as knowledge holders and subjects them to testimonial and hermeneutical injustice. We examine 20 testimonies of injustices experienced by small-scale fisheries people collected by the Global Research Network “Too Big To Ignore” (TBTI) and suggest a glossary of new concepts that can be used to interpret these experiences. Our results exemplify the presence of epistemic injustice, emphasizing the need to associate injustices in small-scale fisheries with non-conventional terms or concepts. We discuss the contribution of transdisciplinary research for providing such concepts and the potential role of social scientists and action researchers to enhance collective hermeneutical resources and thereby advance the goal of Blue Justice for small-scale fisheries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.031 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.064 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".