What a Small Group of People Can(’t) Do
Bibliographic record
Abstract
A growing body of scholars in natural resources management have called for the examination of the roles of social capital and social networks in the effective maintenance of community-based projects. Yet, the role of social capital in collective action cannot be effectively understood without studying agency. The goal of this study is to examine how agents’ individual characteristics and their structural social capital, along with broader cognitive social capital elements, shape possibilities for empowerment at the community level. Drawing from an embedded comparative case study of two community-based ecotourism projects in Ghana, we employed a mixed-methods approach combining Lin’s social capital model and Krishna’s agency model to identify and characterize legitimate agents of change in each community, as well as to evaluate the structure of their discussion and nomination networks (i.e., structural social capital). Differences between communities in the network structure of agents, as well as in their types and levels of engagement, resourcefulness, visions and perceptions of socio-ecological context, exposed key barriers to social capital mobilization. Overall, our results indicate that greater community empowerment is reported where greater community trust and a greater cohesiveness of agents with access to external resources are reported. Altogether, this study adds to past efforts in illustrating how a mixed-methods examination of change agents in a CBNRM setting can surface internal opportunities for and constraints on social capital mobilization toward community empowerment.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".