Identifying and addressing the challenges of early-career ocean professionals during the MSEAS conference
Bibliographic record
Abstract
Abstract In recent years, there has been a growing international effort to address the challenges related to the sustainable use of the ocean. To this end, policy-relevant socio-ecological systems research has been evolving to tackle complex, multi-dimensional problems in marine science and ocean policy. The associated transformation of research practices introduces new challenges in contemporary scientific work, particularly for early-career ocean professionals (ECOPs). This paper explores three key challenges raised by ECOPs during the Marine Socio-Ecological Systems (MSEAS) symposium, along with insights gained from discussions with advanced career researchers working in international organizations such as PICES, ICES, IPBES, IPOS, and FAO. The first challenge addresses working in interdisciplinary research; the second relates to the development of communication skills; the third emphasizes the importance of international collaboration across countries and research disciplines, and integration into international organizations and processes. These challenges are acknowledged by the international organizations, and efforts are underway to better incorporate ECOPs into existing frameworks and institutions. Additionally, the discussions gave practical advice to ECOPs, such as clarifying professional objectives early in one’s career in order to better navigate marine socio-ecological research and to step outside of one’s comfort zone while being aware of the risks.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 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".