Safeguarding biodiversity: what is perceived as working, according to the conservation community?
Bibliographic record
Abstract
Abstract Dramatic increases in human populations and per capita consumption, climate change, overexploitation of marine and freshwater resources, and deforestation have caused a litany of negative consequences for biodiversity. Such doom-and-gloom scenarios are widely known, frequently cited and frankly depressing. Although accurate assessments of threats have clear value for intervention planning, we believe there is also a need to reflect on successes. Such reflection provides balance to negative scenarios and may shift attention towards constructive, positive action. Here we use a systematic evaluation of 90 success stories provided by conservation scientists and practitioners to explore the characteristics of the projects perceived as being associated with success. Success was deemed to have occurred for 19.4% of the projects simply because an event had occurred (e.g. a law was passed) and for 36.1% of projects quantitative data indicated success (e.g. censuses demonstrated population increase). However, for most projects (63.9%) there was no evaluation and success was defined by the subjective opinion of the respondent. Conservation community members viewed successful projects most often as those being long-term (88%), small in spatial scale (52%), with a relatively low budget (68%), and involving a protectionist approach alone or in combination with another approach. These results highlight the subjectivity of definitions of success in conservation but also the characteristics of conservation efforts that the conservation community perceives as indicative of success.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".