Human dignity: A fundamental guiding value for a human rights approach to fisheries?
Bibliographic record
Abstract
Recently, a human rights approach has been center-staged within fisheries governance as a response to the limits of private property rights in reducing insecurity and vulnerability among fishers and fishing communities. Despite its growing adoption in international legal frameworks and among civil society organizations, the conceptual pitfalls of the human rights approach to fisheries (i.e., its neoliberal tendencies and the neglect of collective rights and social duties) raised by critical scholarship remain largely unsettled, leading to practical concerns about whether such a framework will ultimately benefit fishers on the ground. To further contribute to the debate, this article presents a nuanced discussion of the human rights perspective by introducing the concept of human dignity. Specifically, it argues that human dignity, with its greater conceptual scope and depth, could act as a foundational value with which to mitigate some of the shortcomings of the human rights approach. The purpose here is suggestive rather than definitive and is aimed at highlighting the link that has not been clearly made between human rights and human dignity. I argue that heightened attention to human dignity has the potential to create wider support for the human rights approach and ultimately help facilitate its efficacy in fisheries.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.102 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".