Using perceptions as evidence to improve conservation and environmental management
Bibliographic record
Abstract
The conservation community is increasingly focusing on the monitoring and evaluation of management, governance, ecological, and social considerations as part of a broader move toward adaptive management and evidence‐based conservation. Evidence is any information that can be used to come to a conclusion and support a judgment or, in this case, to make decisions that will improve conservation policies, actions, and outcomes. Perceptions are one type of information that is often dismissed as anecdotal by those arguing for evidence‐based conservation. In this paper, I clarify the contributions of research on perceptions of conservation to improving adaptive and evidence‐based conservation. Studies of the perceptions of local people can provide important insights into observations, understandings and interpretations of the social impacts, and ecological outcomes of conservation; the legitimacy of conservation governance; and the social acceptability of environmental management. Perceptions of these factors contribute to positive or negative local evaluations of conservation initiatives. It is positive perceptions, not just objective scientific evidence of effectiveness, that ultimately ensure the support of local constituents thus enabling the long‐term success of conservation. Research on perceptions can inform courses of action to improve conservation and governance at scales ranging from individual initiatives to national and international policies. Better incorporation of evidence from across the social and natural sciences and integration of a plurality of methods into monitoring and evaluation will provide a more complete picture on which to base conservation decisions and environmental management.
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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 | 0.124 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".