Empowering Early Career Ocean Professionals as Ambassadors of Ocean Best Practices
Bibliographic record
Abstract
Early-career ocean professionals (ECOPs) are integral in shaping the future of ocean leadership. As they progress through the critical learning stages of their careers, ECOPs are often exposed to diverse, new, and innovative research methods. The Ocean Best Practices System (OBPS) is an international platform to foster exchange, development, convergence and endorsement of methods and practices. The concept of “best practices” in ocean science is a good means to share experiences and understand how practices mature and are adopted. It is, of course, highly context-dependent. The OBPS Ambassador Programme was created to promote cross-generational collaboration in developing best practices, especially within regional contexts. This process is often complex and requires nuanced strategies in knowledge exchange, mentorship, and professional development. Nevertheless, it has led to valuable contributions from ECOPs, increased their representation, and enhanced regional awareness of best practices. Through the Ambassador Programme, we aimed to rethink how we communicate, co-design methods, set priorities, and create practices that are inclusive and globally relevant.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".