Artisanal Products and Land-Use Land-Cover Change in Indigenous Communities: The Case of Mezcal Production in Oaxaca, Mexico
Bibliographic record
Abstract
Artisanal products are considered an alternative to industrial production; however, upon entering global commodity markets, pressures are placed on the territories and customary governance of producer communities. Through the lenses of land system science and telecouplings, this paper examines the links connecting global markets and artisanal products, using the case of mezcal production in an Indigenous community in Oaxaca, Mexico, and the resulting impacts to LULC (land-use and land-cover) dynamics and associated governance. Data were collected through document review, semi-structured interviews, and LULC analysis comparing the years 1993, 2001, 2013, and 2019. Agave crops expanded from 6 to 14% during 1993–2001, stabilized through 2001–2013, and expanded from 14 to 22% during 2013–2019. Market dynamics played a crucial role in the resultant LULC changes, with the biggest impact on tropical dry forest (TDF). The LULC results were coupled with tequila markets during the first two periods, while the third period was linked to new mezcal markets. Our research shows how artisanal production can drive LULC changes. However, customary governance institutions can mediate the relationship between producers and markets to support more sustainable management of territorial resources, including TDF as an ecologically important but locally undervalued forest type.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".