Analysing the Outcomes of the Forestry Sector on the Sustainable Development Goals for Rural Communities: A Case Study of Cabrero, Chile
Bibliographic record
Abstract
The forestry sector will play a key role in the achievement of the 2030 Agenda for Sustainable Development. The forestry sector is a pillar of many countries’ economies, including Chile. Despite the many economic benefits of the sector for the country, its contribution to the achievement of the Sustainable Development Goals (SDGs) is not clear, especially for rural communities that co-exist with the sector. This study therefore aimed to identify the impacts of the sector on the sustainable development of rural communities in a Chilean commune, Cabrero, and link these impacts to the SDGs. Based on a mixed-method approach that considers key stakeholders perspectives, the paper finds that the forestry sector has multiple and complex impacts on sustainable development, with both positive and negatives outcomes for rural communities, generating synergies and trade-offs with all of the 17 SDGs. It concludes that while companies play a key role in delivering the 2030 Agenda, the achievement of the SDGs will require collaboration amongst people, government and the private sector to understand and support the delivery of a forestry sector that contributes to the sustainable development of communities in Cabrero and, more generally, in Chile.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".