Environmental Alternatives for Rural Development: The Case of Oaxaca, Mexico
Bibliographic record
Abstract
This article examines various environmental alternatives within the context of forest resource-dependent communities of the State of Oaxaca, Mexico. Two main objectives were to describe particular rural development routes among different Mexican communities, and to explain why certain environmental options for rural development are selected over others. While many communities choose either sustainable or illegal logging options in Oaxaca, some may decide against logging of any kind. Four principal forest-based community categories, according to a government forest classification scheme, are discussed in the context of environmental alternatives for this article. Based on this typology, two "integrated forest management" communities in the Sierra Norte of Oaxaca are described and compared. One community's decision not to log within a shared land arrangement has caused significant tensions in the region. Key findings illustrate the extent to which rural communities can make appropriate environmental decisions and examines their effects on environmental and social sustainability. Increased rural involvement in environmental decision-making is called for, since rural residents are those most likely to appreciate nearby natural resources as a source of sustainable livelihoods. It is expected that this research may be applicable to rural areas of other countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".