Advancing the cause? Contributions of criteria and indicators to sustainable forest management in Canada
Bibliographic record
Abstract
The aim of the paper is to take stock, based on my personal scholarly and practical experiences, of the progress made in Canada with criteria and indicators of sustainable forest management (C&I-SFM). Some developmental history is reviewed, and applications at national and local levels are summarized. In my opinion, Canada's work in developing and applying C&I-SFM has been beneficial, particularly in focussing forest-sector dialogues, in sensitizing people to the wide range of forest values, and in retrospective determinations of progress in SFM. Improvements over the next decade are needed in several areas: (a) improving data-collection programs; (b) linking C&I-SFM more directly into forest policy development; (c) shifting from retrospective to prospective sustainability analysis; and (d) applying C&I-SFM to non-industrial forests such as protected areas and urban forests. The C&I-SFM concept is sound. We have yet to tap its full potential in the pursuit of forest and forest-sector sustainability.
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.
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 | 0.037 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".