World Fish Migration Day Connects Fish, Rivers, and People – From a One-Day Event to a Broader Social Movement
Bibliographic record
Abstract
Abstract Widespread declines in migratory fish highlight the need for increased global efforts to raise awareness of their value and abate threats they face. World Fish Migration Day (WFMD), coordinated by the World Fish Migration Foundation, is a biennial global celebration of open rivers and migratory fish achieved through locally organized events with the common theme of connecting fish, rivers, and people. Since 2014, over 1,200 events have been organized in 80 different countries across all inhabited continents. Here we provide an overview of the WFMD social movement, highlighting its ability to raise awareness surrounding the plight of migratory fish. We provide pertinent case studies to illustrate the creative events held throughout the world intended to build the public and political will to enable protection and restoration of migratory fish populations. From a coordination perspective, there are several key principles that underlay the success of WFMD, including taking an optimistic approach, identifying change-makers in the community, and carefully timing the growth of the movement. By reflecting on the approach and growth of WFMD, we feel this perspective piece will prove useful to other groups and organizations considering using the power of social movements to achieve common goals related to environmental conservation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".