Forestry sector, alternative for peace and sustainable development in Colombia. Coffee region case
Bibliographic record
Abstract
The study focused on the potential role of the forest sector in Columbia's post-conflict processes based on the multifunctionality of forests and their components: communities that live in and close to forests, economic dynamics, social actors and sectoral policies. The analysis covered the national level and the coffee region, located in the center of the country. Materials and methods included semi-structured interviews with international and national forest experts. Experts agreed that the forest sector in Colombia represents an alternative pathway for increasing employment and improving the quality of life of local populations, especially in those regions where the post-conflict process is still in effect. In the case of the coffee region, there is a reforestation potential of 54,500 ha and, with minor restrictions, the potential for 164,130 ha of forest plantations. Less than 10 % of that potential has been achieved. Likewise, there are opportunities to implement ecosystem services programs in public and private natural forests that cover 55 % of the coffee region. These potentials are not currently part of the regional priorities, although they could generate income and employment for vulnerable coffee-growing families and for indigenous communities living near natural forests, for whom poverty is a constant due to structural deprivations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".