Using Q-methodology to bridge different understandings on community forest management: lessons from the Peruvian Amazon
Bibliographic record
Abstract
Community forest management (CFM) is promoted as a strategy to reach multiple development outcomes including the sustainable use of forest resources, forest conservation, poverty alleviation, and social equity through the devolution of rights to forest-dependent communities. Developing effective and equitable strategies to promote CFM requires consensus on its goals and the approaches for reaching those goals. Finding common ground among diverse actors involved in the promotion of CFM can be a challenge when their multifaceted expectations and beliefs are not explicitly enunciated or consciously expressed, obscuring contradictions, conflicting objectives, or even shared agendas. An initial step to reaching consensus would be to clarify the range of perspectives that exist to identify common ground and areas of divergent opinion. We report on an initiative applying Q-methodology as a means of identifying differing perspectives on CFM through interviews with 34 informants representing 6 stakeholder groups involved in the promotion of CFM in the Peruvian Amazon: Indigenous leaders, government policymakers, technicians from non-governmental organizations (NGOs), university professors, forestry students, and representatives of donor agencies. We found four different perspectives on what CFM should do: balance conservation with community rights, encourage capacity and enterprise development, technical oversight to protect forests on behalf of Indigenous communities, and support for grassroots Indigenous autonomy. These perspectives revealed differences in how conservation should be achieved and where balance between technical requirements, Indigenous environmental management, and stewardship practices should be favored. Despite different viewpoints, the perspectives also revealed shared understanding of CFM as a mechanism that could emphasize both supporting community rights and conservation goals. This example illustrates how Q-methodology can generate information on the range of perceptions underlying broad strategies such as the promotion of CFM that can facilitate dialogue around shared pathways and agendas.
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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 | 0.106 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".