A tale of two forests: why forests and forest conflicts are both growing in Chile
Bibliographic record
Abstract
SUMMARY Over the past 40 years Chile has implemented a set of forest policies that have been very successful in generating economic benefits. Yet the reasons for that success are also at the root of the growing conflicts around forestry. The main policy has been the promotion of exotic plantation forests that has resulted in the development of a significant export-oriented forest sector, whose ownership is highly concentrated. The expansion of plantations has had negative socioeconomic and environmental impacts on local communities and indigenous peoples, resulting in growing inequalities and conflicts at the local level. Native forests, while important contributors to local livelihoods, have received far less policy attention. For Chile to prosper, policymakers need to better consider how native forests can contribute to local economies, while exportoriented forest companies must find sustainable ways to mitigate or avoid their negative impacts. Without a rebalancing of forest policies, these divergent outcomes will continue to exacerbate local conflicts, compromising the long-term sustainability of both 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".