Improving monitoring and evaluation in conservation and development efforts
Bibliographic record
Abstract
Efforts to alleviate poverty and conserve biodiversity require reliable methods to \nmonitor and assess changes in conservation and development status. Projects intended to \nachieve biodiversity conservation and poverty alleviation objectives often fall short of \ntheir goal. Considering the investments made to support these efforts, this is a real concern to society. Evaluating the effectiveness of these efforts is crucial to receive \nongoing support and to learn what’s working, what’s not and how it can be improved. This research examines current M&E and impact assessment practices and systems at conservation and development organizations garnered through a survey and interviews \nand documents opinions, experiences and lessons learned from key informants. \nOrganizations are facing common barriers but share opportunities to improving M&E. \nEmbracing a culture of learning, synthesizing a common vocabulary and implementing organizational change are important steps in improving M&E practice to provide \ninformation and data for better M&E and impact assessment.
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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 | 0.185 | 0.301 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".