Sources of Change in Community Forestry - The Roles of Learning and Beliefs in the Policy Process: A Comparative Analysis of Ecuador, Mexico and Canada
Bibliographic record
Abstract
Community forestry has become a prominent policy instrument over the past decades as a response to deforestation pressures and rural poverty. Its political implementation involves a complex process with a profound structural change - away from state-based forestry to locally based decision-making authority. The research analyzes the internal development among policy actors in order to understand how community forestry can emerge in a regional policy system. It explores three different case studies with distinct policy processes towards community forestry: an international development project (Ecuador: Esmeraldas), a grassroot environmental movement (Canada: British Columbia), and an institutional restructuring process (Mexico: Quintana Roo). The theoretical approach is based on the Advocacy Coalition Framework (ACF). The ACF views policy change as a result of competing advocacy coalitions which act according to their policy beliefs. Policy change can be affected by internal changes (policy learning and changes of beliefs) or by external perturbations which affect the power constellation between the coalitions. Each policy process is analyzed over more than a decade, based on empirical data from semi-structured interviews with key actors and complemented by literature. The major actors and coalitions are identified, as well as their learning and changes of beliefs over time to understand their influence on the policy process. In summary, the research found that policy learning has a high importance for the internal development of community forestry policy, while often hidden behind the strong presence of an external perturbation. Although not as a singular force, policy learning has been shown to have a very potent role in enhancing, or sustaining, policy changes. Policy learning can have a stabilizing effect against adverse events, once the implementation process has started (Mexico). Policy learning can even generate the major momentum of change that unfolds when released by an external catalyst event (Canada). It can also, however, have a very limited influence, if not form an internal challenge to the implementation (Ecuador). It has been found that a change of policy beliefs is not a requirement for policy change in the early stages of community forestry, and cannot be indicated by visible changes in the policy network or local forestry arrangements. These might concern other adjustments of the actors to the changes, which do not reflect their substantial policy beliefs. The changes of policy beliefs in the three case studies show some similar patterns in the way the Community Forestry Coalition developed their policy goals over time, as well as in the way the State Forestry Coalition adjusted strategically to the new impact of the CFC on the network. The research discusses three distinct strategic approaches of the coalitions and their varying success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".