Assessing United Nations conservation-oriented days, years and decades through the lens of a change model
Bibliographic record
Abstract
Summary Since the 1950s, the United Nations (UN) has designated days (e.g., World Wetland Day), years (e.g., Year of the Gorilla) and decades (e.g., Decade on Biodiversity) with a commonly stated goal to raise awareness and funding for conservation-oriented initiatives, and these Days, Years and Decades of ‘…’ (hereafter ‘DYDOs’) continue. However, the effectiveness of these initiatives to achieve their stated objectives and to contribute to positive conservation outcomes is unclear. Here we used a binary analysis change model to evaluate the effectiveness of UN conservation-oriented DYDOs observed between 1974 and 2020. We also examined four case studies to understand the different strategies employed to meet specified conservation goals. We found that DYDOs apparently contributed to positive conservation outcomes when they were tied to social media campaigns and/or when they were strategically situated in current events or global discourse. Although the outcomes of DYDOs were varied, those with longer timescales and those that engaged local communities were more likely to be successful. We suggest that DYDO organizers should identify all possible paths of action through the lens of the change model outlined in this paper to strengthen the value and effectiveness of these initiatives in the future. Using this approach could help ensure that resources are used efficiently and effectively, and that initiatives yield positive conservation outcomes that benefit people and nature.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".