Bibliographic record
Abstract
Target setting has become a familiar concept through the international policy debate on global climate change. In contrast with greenhouse gas emission targets, the type of targets we emphasize in this book must be developed from ecological knowledge rather than from a socio-economic analysis and a desired outcome. The focus on value-free, quantitative approaches to target setting we imposed from the start could not be applied to all chapters, however. Contributors to this book represent a wide array of professional backgrounds and, accordingly, they approached conservation target setting from a variety of perspectives. Hence, we were not surprised when some contributors argued that targets should integrate socio-economic considerations. Does this reflect insubordination on their part or, rather, the complex socio-economic ramifications of conservation issues? It would be naive to expect a large group of intellectuals to abide by a rule and, therefore, we suspect that both hypotheses may apply here! Including ourselves, most of this book's contributors work with forest managers and policy-makers on a weekly basis unless they are practitioners themselves. Thus, they are well aware of the practical limits to the development of conservation policy and its implementation. None the less, to paraphrase George Bush, we as co- editors decided to stay the course and separate ecological and socio-economic considerations in the target-setting approaches presented in the various chapters. Our stance on this issue generated interesting discussions, which provided insight for this synthesis.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".