Strategies and best practices for fostering diverse engagement in international collaborations
Bibliographic record
Abstract
Given that ocean-related challenges are increasingly globalized, complex, and interconnected, international collaboration is essential to move toward a sustainable future and to achieve the overarching goals of the UN Decade of Ocean Science for Sustainable Development. To effectively address these challenges, it is crucial to engage diverse people from different regions, backgrounds, and sectors. However, there is still a lack of integration of experiential knowledge and diverse perspectives in ocean science to enhance the full participation of marginalised and economically disadvantaged groups. These experts are often excluded from discussions regarding ocean governance and management strategies in most countries globally. To overcome these barriers, some organizations have already established policies and practices to foster diversity, equity and inclusion (DEI). However, these practices often only exist in formalized documents but are not adopted in practice due to deeply rooted organizational culture and systemic biases. Further, these practices are not transparent or easily accessible, which impedes other institutions and projects from building on existing knowledge to identify transformative and universal solutions. To increase diverse participation in ocean science, gaps should be identified and addressed. The UN Ocean Decade ECOP DEI task team comprises a diverse group of professionals spanning many regions of the world and connected to various Ocean Decade Programmes. Together with the endorsed Ocean Practices programme, the task team is exploring innovation in how we, as a community of ocean professionals, conduct co-design in the context of co-producing knowledge and co-developing solutions. Here, we present a gap analysis in the form of a literature review and results from a survey across all Ocean Decade actions on effective strategies to advance diversity, equity, and inclusion. We identify strategies that are cross-cutting, and interoperable and can thus become standardized best practices advancing DEI within the Ocean Decade. Our aim is to support a transformational change in how Ocean Decade Challenges can be collaboratively addressed. The presented results from the survey capture the needs and existing solutions for fostering diverse participation in the co-design of ocean initiatives, offering actionable insights for informed decision-making. Additionally, the survey highlights the specific needs of the ocean professional community and showcases the progress, challenges, and tools that organizations are using to achieve DEI-related goals within the Ocean Decade. Our aim is to make these practices findable, accessible, interoperable and reusable (FAIR), thereby advancing inclusivity and eliminating discrimination in this transformative period. By creating avenues for collaborative knowledge-sharing relating to DEI in the Ocean Decade, this work will broadly apply to partner organizations striving to make visible and meaningful progress for a more inclusive, accessible 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 | 0.158 | 0.155 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.043 | 0.039 |
| Open science | 0.009 | 0.037 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".