Innovation in Communications about Marine Protection
Bibliographic record
Abstract
1. Most of the people working in the field of marine protection share a common goal: that decision makers, stakeholders, and the public should see marine protection as a priority and dedicate a portion of their attention and resources to it, making decisions and taking actions that reflect the value of marine protection to ecological and human well-being. 2. If this goal is to be achieved, the field of marine protection needs to embrace the field of communication in a more concerted manner. 3. In this paper we outline some of the latest trends, principles and issues relevant to communication in marine protection and illustrated these with a range of examples. We discuss key themes emerging from our review. 4. We outline a number of strategies for strengthening the role of communications, including means for those involved in marine protection communications to connect with each other, increased testing and sharing of examples, the use of grounded theory methods to continuously define lessons and principles, and ways to increase coordination between marine protection organizations. 5. It is our hope and intention that this paper will mark the beginning of a stronger cross-disciplinary field of study, and that such a field will in turn advance marine protection locally and globally. 6. Readers can contribute to this goal and emerging field by connecting with each other and with us around strategies, ideas and examples.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".