Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The institutions created to address the problems associated with forest utilisation, degradation and destruction internationally have ranged from hard laws, put in place by governments through legislation, to soft law mechanisms such as forest certification. Each of these standards requires enforcement and evaluation through monitoring and information reporting. Despite differing levels of legalisation, the monitoring and information reporting requirements documented in hard and soft law mechanisms can reveal considerable crossover. It is therefore important to identify where hard laws are adequate for individual SFM situations and where soft laws are performing better, with a view to identifying overlaps that affect the efficiency, cost effectiveness and level of confidence in the monitoring process. This paper presents a basic theoretical background to SFM and discusses two important characteristics, namely adaptive management and monitoring. The hard and soft law mechanisms available to forest policy makers are addressed and, finally, the role of stakeholders and the wider operating environment in forestry-related monitoring is considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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 it