Metrics and outcomes of conservation education: a quarter century of lessons learned
Bibliographic record
Abstract
We conducted a systematic literature review to analyze evaluations of conservation education programs on a global scale in order to better understand (1) temporal and spatial trends in conservation education program evaluations over the last 25 years, (2) patterns in the types of conservation-related issues addressed through these programs, (3) metrics that indicate effectiveness of conservation education programs, and (4) methods and timeframes used to draw conclusions about program outcomes. Findings indicated that there is a need to better connect the types of issues addressed through conservation education programs with metrics that would indicate success in addressing these issues and the actual outcomes measured and reported. As well, there is an opportunity to employ a variety of metrics and methods for evaluating program outcomes, particularly in developing countries, by focusing on cognitive and behavioral components as well as social and ecological ones. Finally, shifting to a more comprehensive strategy for evaluating multiple outcomes in different cultural contexts would provide opportunities for utilizing mixed methods and qualitative approaches in partnership with community stakeholders.
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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 | 0.183 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".