Scaling up in community forest enterprises: the case of central Mexico
Bibliographic record
Abstract
Abstract For community forest enterprises (CFEs) in central Mexico, ‘scaling up’ can be an effective means of achieving the transition to economically attractive and sustainable forest management, but little is known about the potential and challenges that they face in this regard. We used a qualitative case study to evaluate a set of variables that determine the limitations and opportunities for scaling up CFEs in central Mexico and thereby expanding their commercial capacities, activities and outputs. The framework included concepts related to sustainable forest management, natural resource governance and temperate forest ecology. We interviewed leaders of four communities (n = 30) and 15 external actors (regional industry, and national non-governmental organizations). Communities that had developed long-term plans for forest management that embrace conservation values were also those with the greatest capacity to generate sustainable income streams from diverse sources. The robust legal frameworks and community institutions that set up procedures for responsibly harvesting and selling timber, thereby generating income, offer opportunities to enhance the effectiveness of CFEs. Demand continues to grow for wood products involving skilled crafts in central Mexico, but local production remains low; a lack of access to finance imposes limits on investment in the forests and value-added options for the products and services. Market opportunities and growth are also restricted by substandard physical infrastructure (e.g., roads, electricity) limited access to finance (e.g., credit, private-sector investors), and an absence of business plans. There are no formal networks to facilitate learning among these CFEs. Scaling up for these CFEs will require access to markets, innovations, and finance to create sustainable value chains for wood and non-wood forest products. The Mexican government could be instrumental in this by incorporating the notion of scaling up for CFEs when enacting policy that builds on and supports the country’s proven models of community-based forest management. On the other hand, this approach can be useful for developing more robust theoretical and methodological frameworks that capture these complex dynamics, contribute to the theory and practice of enterprise forestry development, sustainable natural resource management, and effective policy formulation.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".