Transnational networks and the adoption of model forests in Argentina
Bibliographic record
Abstract
How are international environmental ideas adopted locally? We an- swer this question by examining the adoption and development of the model forest idea in Argentina since the late 1990s. The concept of model forest was born in Canada in 1991 as the brand name of a new national program aimed at promoting the building of local-level governance processes and arrangements for sustainable forest management. The idea soon started trav- elling worldwide thanks to the Canadian international cooperation agen cies initiatives and became a benchmark of UN programs. Argentina was an early adopter of the model forest idea: in 1996 the Argentine Secretaria for the Environment signed a letter of intent with the International Model For est Network. As a result, six model forests formed throughout the country between 1998 and 2008. We argue that transnational networks of bureau- crats, advocates, and stakeholders help explain how natural resources gover- nance programs travel across countries. We distinguish more technical-driven adoptions from societal-driven ones, as a function of existing levels of con- flict. We expect technical-driven adoptions to take place in contexts of lower levels of conflict and societal-driven adoptions in contexts of higher levels of conflict. This paper is a first step in a broader project that compares the adoption and evolution of community-based forests in Latin America.
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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 | 0.003 | 0.005 |
| 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 | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".