Bibliographic record
Abstract
The regional node of the ECOP Programme in Asia (hereinafter referred to as “ECOP Asia”) was informally established in June 2021, during the Virtual Early Career Ocean Professionals Day (V.ECOP Day). At the time, ECOP Asia was composed of a dozen volunteer members from countries across East, South and Southeast Asia, including Bangladesh, China, India, Indonesia, Japan, Malaysia, Philippines and the Republic of Korea. The core team met on a monthly basis with the aim of connecting interdisciplinary groups of ECOPs across the continent, and sharing knowledge and experience about the recently launched United Nations Decade of Ocean Science for Sustainable Development (2021-2030) (hereinafter referred to as the “UN Ocean Decade”). Members of the informal 2021 ECOP Asia group were particularly active and motivated to spread the word about ECOPs and the UN Ocean Decade. Among other contributions, they circulated the first pan-Asia ECOP survey in early Spring 2021 (with region- and country-specific results available in this report1), celebrated “World Oceans Day” on 8 June 2021 by sharing inspiring testimonials from ECOPs across India (video), made short awareness-raising interventions during online webinars and workshops, and moderated a Decade Action Incubator Session dedicated to ECOPs during the UN Decade Regional Kickoff Conference for the Western Pacific and its Adjacent Areas, a two-day conference co-sponsored by the Intergovernmental Oceanographic Commission of UNESCO (IOC-UNESCO) and its sub-commission for the Western Pacific (WESTPAC). The latter session provided perspectives on science communication, inviting speakers from different career stages and ocean sectors2 (video recording is available here). On November 2021, the IOC-UNESCO launched a call for individual consultants to support a variety of tasks related to the development of the regional dimensions of the ECOP Programme, seeking coordinators in Africa, Asia, Oceania and the Caribbean Small Island Developing States (SIDS)3. A regional consultant was subsequently hired for Asia in mid-December 2021 and has remained in this position since then. The hiring process coincided with the official launch of the ECOP Programme website, including dedicated web pages for the first three regional and national ECOP nodes: Africa, Asia and Canada4. By November 2022, a national node in Japan and regional hub in Central America were also established, followed closely by a new regional chapter in the Caribbean. Throughout the first half of 2023, another nine additional national nodes have emerged from all over the world, including in Brazil, Italy, Morocco, Niger, Nigeria, the Republic of Korea, Senegal, Togo and the United States. Several other ECOP chapters are waiting in the pipeline, currently building their own core teams, drafting concept notes, and engaging with their respective communities (e.g., Australia/New Zealand, Belgium, China, Europe, India, Liberia, Malaysia, Mexico, Mozambique, Spain and the United Kingdom). Extending from the Middle East all the way to Indonesia in Southeast Asia, and including the Russian Federation5, the ECOP Asia community grew significantly throughout 2022 and in the early months of 2023 (see section I). Figure 1 below provides a non-exhaustive list of 2022 activities and highlights that contributed to the development and expansion of ECOP Asia since the appointment of the regional consultant/coordinator. Table 1 lists more recent updates and achievements since November 2022, including ongoing work in 2023.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.131 | 0.089 |
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".