From ocean knowledge to blue citizenship for all: bottlenecks and way forward
Bibliographic record
Abstract
To address the current and future challenges facing the ocean, there is a need to raise wider public attention to ocean sustainability and strengthen blue citizenship, addressing both marine and freshwater in an integrated perspective from source to sea. Recent years have seen more resources allocated to both formal and informal blue education initiatives. However, to date, there exists limited understanding and focused attention on identifying the enabling conditions of blue education initiatives that lead to measurable impact. These limitations refer mainly to the identification of bottlenecks and strategies for upscaling successful initiatives and mainstreaming the “blue” into the national education systems, so that every school, child, teenager and student (as citizens and future generation leaders) are reached.This presentation assesses the state of play of blue education, building on a critical review of different blue education experiences globally, covering both the scientific and grey literature. This review highlights: (1) some of the success stories and diversity of blue education experiences in several regions across the globe (2) bottlenecks encountered by these experiences; (3) solutions or mechanisms that would help amplifying and mainstreaming blue education initiatives; (4) current knowledge gaps. Finally, this presentation gives recommendations for supporting effective integration of ‘blue’ into education initiatives and systems. It highlights also opportunities for future research and evaluation of blue education initiatives at all various scales.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".