Towards greater transparency and coherence in funding for sustainable marine fisheries and healthy oceans
Bibliographic record
Abstract
This final manuscript in the special issue on “Funding for ocean conservation and sustainable fisheries” is the result of a dialogue aimed at connecting lead authors of the special issue manuscripts with relevant policymakers and practitioners. The dialogue took place over the course of a two-day workshop in December 2018, and this “coda” manuscript seeks to distil thinking around a series of key recurring topics raised throughout the workshop. These topics are collected into three broad categories, or “needs”: 1) a need for transparency, 2) a need for coherence, and 3) a need for improved monitoring of project impacts. While the special issue sought to collect new research into the latest trends and developments in the rapidly evolving world of funding for ocean conservation and sustainable fisheries, the insights collected during the workshop have helped to highlight remaining knowledge gaps. Therefore, each of the three “needs” identified within this manuscript is followed by a series of questions that the workshop participants identified as warranting further attention as part of a future research agenda. The crosscutting nature of many of the issues raised as well as the rapid pace of change that characterizes this funding landscape both pointed to a broader need for continued dialogue and study that reaches across the communities of research, policy and practice.
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 | 0.181 | 0.350 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.041 | 0.031 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".