Shaping the future of marine socio-ecological systems science: combining interdisciplinary and transdisciplinary approaches and knowledge co-creation with diverse stakeholders
Bibliographic record
Abstract
Abstract Following the first symposium held in Brest, France, in 2016, the second Marine Socio-Ecological Systems Symposium (MSEAS) was held in Yokohama, Japan, in 2024, after 4 years of postponement due to the COVID-19 pandemic. In 2016, interdisciplinary efforts to inform ocean governance using the Social-Ecological System (SES) approach was highlighted as highly necessary. MSEAS 2024 emphasized the combination of interdisciplinary and transdisciplinary approaches, exploring and developing new insights in co-designing research and co-producing solution-oriented knowledge, while involving diverse teams and stakeholders. The symposium covered a range of topics, including methods and assessment strategies of how to interpret social–ecological systems, stakeholder perceptions and how to communicate the research, involvement of communities, and the co-creation of science. Additionally, the symposium featured inspirational events for Early Career Ocean Professionals (ECOPs) and explored art–science connections. This graphical record aims to convey the essence of the symposium to a broad audience through illustrations. These graphics by a Japanese illustrator are the result of a participatory process during the conference, based on interviews with session conveners and contributions from participants via an online form.
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 | 0.052 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".