Legitimacy of Public Domain Forest Taxation, and Combatting Corruption in Forestry
Bibliographic record
Abstract
Taxation systems for publicly-owned forests should be founded on a government's constitutional right of radical tenure, simultaneously recognising the rights of forest-dependent stakeholders to share benefits and responsibilities. Complex taxes may be historically explicable but are open to corrupt diversions. A systems approach may be essential for benefit sharing to really reach geographically or institutionally remote beneficiaries. The purpose of each tax should be justified and communicated to tax payers, including through participatory development of tax laws. Transparency in justification and implementation of forest taxes helps to diminish corruption through replacement of administrative discretion by rule-based decision making. Strategists should be more aware of the links between criminality in various sectors.
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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 | 0.026 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 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".