Should sustained yield be part of sustainable forest management?
Bibliographic record
Abstract
This paper considers the question of whether sustainable forest management (SFM) should continue to incorporate sustained yield (SY) requirements, as it currently does in many jurisdictions. We evaluate the extent to which SY and SFM are consistent with notions of weak and (or) strong sustainability. Strong sustainability implies placing constraints on the reduction of stocks of natural capital to prevent irreversibility and (or) protect flows of services that have public good characteristics. In contrast, weak sustainability may allow market forces to draw down stocks of natural capital so long as levels of total capital (including human-made and natural capital) are maintained. We argue that with SY policies, we have probably chosen to attach strong sustainability policies to the only forest resource that does not need such protection (i.e., timber), while we have excluded other resources that could well need such protection (e.g., biodiversity) for pursuing SFM. Thus, the concept of allowable annual cuts could be dropped from SFM to be replaced by safe minimum standards on components of forest capital that are subject to irreversibility and (or) that have public good features. In other words, if we truly wish to pursue SFM, it may be necessary to leave SY behind.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".