Role of linkages and diversity of partnerships in a Mexican community‐based forest enterprise
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify and describe the pervasiveness and importance of various types of institutional and organizational interactions across multiple levels for the management of a community forest enterprise. Design/methodology/approach The paper analyzes a long‐standing case in Michoacán, Mexico, the San Juan Nuevo (SJN) enterprise, a community‐based system with a multiplicity of actors, objectives, and partners. Information was collected through 100 semi‐structured interviews. By presenting and discussing the main community‐based development strategy within the overall socio‐political context and achievements of the case, the authors attempt to understand the complexity of cross‐scale institutional and organizational linkages and their role in sustainable resource management. Findings SJN enterprise had linkages with some 22 major partners over the years across four levels of organization: local, state, federal, and international. Cross‐scale partnerships were not merely important, but essential for the overall success of the enterprise in the face of uncertainty over resource ownership and lack of legal jurisdiction. These diverse partnerships and interactions enabled robust institutional structures, making possible the development of linkages to help conserve the resource base and create grassroots socio‐economic development for the comuneros. Research limitations/implications Further understanding of the importance of partnerships and linkages for the development and maintenance of community‐based initiatives will require the analysis of, and comparison between, several long‐standing case studies. Practical implications There is the need to recognize the multiple roles of partnerships, from business networking to research and training, thus unpacking different kinds of capacity building. Actors at various levels can influence management practices in diverse ways, helping to find a balance between local livelihoods and larger conservation needs. Originality/value The paper brings a new approach to analyze how indigenous and other rural communities are “opting‐in” to the global economy, through a diversity of partnerships and a complexity of interactions across organizational levels.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| 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".