Strengthening monitoring and evaluation of multiple benefits in conservation initiatives that aim to foster climate change adaptation
Bibliographic record
Abstract
Abstract As the need to monitor and evaluate progress on climate change adaptation is increasingly recognized, practitioners may benefit from applying lessons about effective monitoring from the conservation field. This study focuses on monitoring conservation interventions that aim to foster climate change adaptation by assessing: what ways practitioners are adopting best practices from monitoring and evaluation (M&E) in conservation; what practitioners are monitoring in relation to reported outcomes; how monitoring comprehensiveness varies in practice and what factors enable more comprehensive monitoring; and practitioner views on what could improve M&E of adaptation actions. We conducted this study using a portfolio of 76 adaptation projects implemented across the United States and employed a mixed‐methods design that included document analysis, an online survey, and semi‐structured interviews. The majority (84%) of projects reported social outcomes at project completion in addition to ecological outcomes (100%), but monitoring plans focused primarily on ecological and biophysical changes. Only 21% of projects connected monitoring metrics to a theory of change linking actions to expected outcomes. Involvement of an external research partner was identified as a key factor in supporting more comprehensive monitoring efforts. Results provide applied insights for enhancing delivery of social and ecological outcomes from adaptation projects, and suggest research pathways to improve monitoring and effectiveness of climate‐informed 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 | 0.143 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".