Bibliographic record
Abstract
While there is growing evidence of the importance of marine ecosystems for our societies, evidence shows also that pressures from human activities on these ecosystems are increasing, putting the health of marine ecosystems at stake worldwide. Hence, Blue Economy is becoming an important component of future socio-economic development strategies (e.g. this is called Blue Growth in Europe), that eventually can result in increasing pressures at sea, and despite the current regulatory framework (in particular with the Oceans Act, in USA or Canada, and the Marine Strategy Framework Directive, in Europe), it is likely that this situation will continue in the future. Ensuring all those connected to the sea, directly or indirectly, gain a better understanding of the importance of the seas, the human-sea interactions and opportunities to act better and reduce impacts from human pressures, is central to Ocean Literacy (OL). Receiving increasing attention in Europe and USA, OL is a challenge for all parts of society: educators & trainers, children and professionals, civil society and scientists, consumers and policy/decision makers. It is seen as part of the package of solutions that will lead to a change in behavior and practice, thus reducing impacts and resulting in healthier marine ecosystems, whilst allowing development opportunities offered by seas are seized in a sustainable manner. This Research Topic focuses on the issues and options for effective OL worldwide. It discusses: (1) existing experiences in OL (formal and informal education for children, training for professionals, tools for raising awareness of consumers – and of investors in the marine sectors…) and their effectiveness (from understanding better to acting differently); (2) the role OL could play (in interaction with innovation, regulation, economic incentive, social norms…) to support human capital development as key component of sustainable growth; and (3) pre-conditions for effective OL for different sectors and target groups. Questions relevant to OL include: Which knowledge – produced by whom – to share and how? Who to target – and how to effectively reach those targeted? How to design OL initiatives – including by mobilizing those targeted (via living lab approaches e.g.) – to ensure effective OL and pave the way for behavior change? What are the knowledge gaps that limit our capacity to design effective OL? As scientists, it is likely you have many more questions to offer and discuss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.166 | 0.068 |
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".