Examining progress towards achieving the Ten Steps of the Rome Declaration on Responsible Inland Fisheries
Bibliographic record
Abstract
Abstract Inland capture fisheries provide food for nearly a billion people and are important in the livelihoods of millions of households worldwide. Although there are limitations to evaluating many of the contributions made by inland capture fisheries, there is growing recognition by the international community that these services make critical contributions, most notably to food security and livelihoods in rural populations in those low‐income countries with extensive freshwater resources. With the increasing appreciation of the key role of inland fisheries to the health and well‐being of human populations globally, the Food and Agriculture Organization of the United Nations and Michigan State University convened the 2015 global conference,Freshwater, fish, and the future – cross‐sectoral approaches to sustain livelihoods, food security, and aquatic ecosystems. What emerged from the interactions between inland fisheries’ scientists, resource managers, policymakers and community representatives from across the world was a forward‐looking call to action culminating with the 2015 Rome Declaration “Ten Steps to Responsible Inland Fisheries” (FAO & MSU,Rome declaration on responsible inland fisheries: 5735E/1/06.16). Four years after this landmark conference and declaration, we seek to advance discussion on the “Ten Steps,” namely what successful implementation looks like, assess current examples of implementation, suggest potential signals of progress and provide some specific, indicative examples of progress for each step. While there are promising signs of progress, we conclude that there remains a strong need to galvanize momentum for sustained action to ensure that inland fish and fisheries are accounted for and incorporated into broader water resource management discussions and frameworks.
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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 | 0.064 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".