Mediating Forest Transitions: ′Grand Design′ or ′Muddling Through′
Bibliographic record
Abstract
Present biodiversity conservation programmes in the remaining extensive forest blocks of the humid tropics are failing to achieve outcomes that will be viable in the medium to long term. Too much emphasis is given to what we term 'grand design'-ambitious and idealistic plans for conservation. Such plans implicitly oppose or restrict development and often attempt to block it by speculatively establishing paper parks. Insufficient recognition is given to the inevitable long term pressures for conversion to other land uses and to the weakness of local constituencies for conservation. Conservation institutions must build their capacity to engage with the process of change. They must constantly adapt to deal with a continuously unfolding set of challenges, opportunities and changing societal needs. This can be achieved by long term on-the-ground engagement and 'muddling through'. The range of conservation options must be enlarged to give more attention to biodiversity in managed landscapes and to mosaics composed of areas with differing intensities of use. The challenge is to build the human capacity and institutions to achieve this.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".