An appeal for a code of conduct for marine conservation
Bibliographic record
Abstract
Marine conservation actions are promoted to conserve natural values and support human wellbeing. Yet the quality of governance processes and the social consequences of some marine conservation initiatives have been the subject of critique and even human rights complaints. These types of governance and social issues may jeopardize the legitimacy of, support for and long-term effectiveness of marine conservation. Thus, we argue that a clearly articulated and comprehensive set of social standards - a code of conduct - is needed to guide marine conservation. In this paper, we draw on the results of an expert meeting and scoping review to present key principles that might be taken into account in a code of conduct, to propose a draft set of foundational elements for inclusion in a code of conduct, to discuss the benefits and challenges of such a document, and to propose next steps to develop and facilitate the uptake of a broadly applicable code of conduct within the marine conservation community. The objectives of developing such a code of conduct are to promote fair conservation governance and decision-making, socially just conservation actions and outcomes, and accountable conservation practitioners and organizations. The uptake and implementation of a code of conduct would enable marine conservation to be both socially acceptable and ecologically effective, thereby contributing to a truly sustainable ocean.
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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 | 0.237 | 0.499 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.059 |
| Scholarly communication | 0.032 | 0.025 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.059 | 0.073 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".