Building Successful International Summer Schools to Enhance the Capacity of Marine Early Career Researchers
Bibliographic record
Abstract
The development of informal science learning programs is a key strategy for supplementing traditional training for early career researchers (ECR). Within the marine sector, there has been a proliferation of international summer schools (a form of informal science learning program) to support ECRs to develop the networks, skills, and attributes needed to tackle ocean sustainability challenges and support the attainment of the Sustainable Development Goals (e.g., collaboration across disciplines, policy engagement, etc.). Yet, there exists very little evidence on the impact generated by such informal science learning programs or the design strategies that can confer their success. This commentary seeks to address this knowledge gap by considering the successful biennial Climate and Ecosystems (ClimEco) marine summer school series that has run since 2008. Specifically, we draw on the perspectives of lecturers and organisers, in combination with a survey of ClimEco participants (𝑛 = 38 ECRs) to understand the drivers and motivations of ECRs to attend summer schools, the types of outcomes and impacts that summer schools can have for marine ECRs, and the key factors that led to the successful attainment of these impacts, outcomes, and benefits. In doing so, we develop guidance that would enable global summer school convenors to effectively support the next generation of marine researchers to advance ocean sustainability.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 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".