Social capital, context, and consensus-building and cooperation in community-based forest management
Bibliographic record
Abstract
Due to its potential to contribute to the sustainability of forest management, community-based forest co-management has become globally acceptable, and some form has been reported in more than fifty countries. The benefits of co-management depend on cooperation among and between a group of local community stakeholders and a local government agency. However, neither cooperation, the consensus-building process through which it develops nor the context of the consensus-building process that influences both has been a focus of past co-management research. The thesis identifies the key criteria that are most important and best represent the consensus-building process as well as the context of the consensus-building process, using a comparative case study of two Local Citizens' Committees (LCCs) who advise a government agency on the development of public forest management plans in Ontario, Canada. Key criteria are identified using network analysis (domain, central and cluster analysis) of cognitive maps developed from participant and agency support staff interviews. Two structural analysis techniques are used to analyse each key criterion's links. Given-Means-Ends (GME) analysis of key consensus-building criteria is used to identify the informal goals of LCC members including the development of cooperation and their perceived influence over consensus-building criteria. GME analysis of key context criteria is used to identify the informal goals of agency support staff and their perceived influence over context criteria. Context, Structure and Performance (CSP) analysis is used to identify the relative influence of key consensus-building criteria on consensus-building and the development of cooperation and LCC performance. CSP is also used to identify the relative influence of key context criteria on context, consensus-building and the development of cooperation as well as performance. Since social capital is relevant to the design of resource management institutions for resource sustainability, an adapted social capital framework is used to re-interpret key consensus-building and key context criteria to generate a cross-case explanation of the development of cooperation. The thesis concludes by describing the two major obstacles to the development of cooperation between LCC members and co-management agency support staff, suggesting improvements to the social capital framework and with lessons and implications for consensus-building and co-management theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| 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".