Bibliographic record
Abstract
There has been considerable growing internationalconcern with illegal forest activities over the lastdecade or so. Illegal forest activities are said tocause massive environmental destruction, deprivegovernments of billions of dollars in lost revenuesand generally undermine the rule of law.The concern for illegal activities in forest thathave resulted in deforestation has, in turn, led toincreased studies in the existing forest laws. Thesestudies have recently suggested that many forestrylaws and regulations can discriminate against smallproducers and that a large number of people dependon small-scale illegal forestry activities to survive. Ithas, therefore, been postulated that enforcing theselaws might potentially harm poor people. It is alsofeared that some government authorities wouldselectively target small producers, truck drivers andforestry workers rather than the big players who areresponsible for most of the real problems.As a result of these concerns, the Centrefor International Forestry Research (CIFOR)commissioned exploratory studies drawn from sixcountries (Bolivia, Cameroon, Canada, Honduras,Indonesia and Nicaragua) to help think through theselegal issues in practice and in different contexts. Thestudies were coordinated and reports synthesizedinto the present final report. The findings of thereport can be summarized in the following thematicareas:
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".