Bibliographic record
Abstract
Since its inception in 1993, the Forest Stewardship Council certification scheme to assess the quality of responsible forest stewardship has aimed to certify both industrial-scale and smallholder forests. This article considers variations in FSC smallholder certification: single or group; Small and Low Intensity Managed Forests (SLIMF); and both company- and community-managed community forests in Global North and South countries. The classification of smallholders, as “subsistence surplus” or “sell-to-survive,” as proposed by the political ecologist Jason Moore, is also applied. Global North smallholders account for two-thirds of smallholder certified area and, in general, are able to meet the costs of FSC certification because of the demand for certified timber, their better socio-economic circumstances, a greater degree of group organization, and, in some cases, access to state subsidies. They are also more likely to be price-makers. Global South forests both house more of the planet’s remaining biodiversity and are more vulnerable to degradation. Economic and social realities dictate that global South smallholders are largely constrained by having to sell their timber to survive and fall in the price-takers category. In the absence of subsidies, price premiums, or a secure value chain, they are unable to afford renewal of FSC certification. The article concludes with an assessment of some realistic options for smallholder forestry certification.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".