Can the center hold? Boundary actors and marginality in a community-based natural resource management network
Bibliographic record
Abstract
Community-based natural resource management (CBNRM) seeks to align the interests of local communities and conservation institutions. A significant challenge to this realignment is that CBNRM is often implemented in locations with colonial histories of oppression, persecution, and dispossession that have left legacies of inequity and marginalization. Social networks are one method for discerning how marginalized CBNRM actors can negotiate entitlements and agency. Through the lens of social networks, marginalization can be viewed as insufficient connectivity between the center and the periphery of the network. One possible remedy to this dysfunction are boundary actors, which are thought to be vital to connecting parts of social networks that would otherwise be poorly connected. Using social network analysis to visualize interactions between the Topnaar community and CBNRM institutional actors in Namibia’s Namib-Naukluft and Dorob National Parks, we find a number of individuals well-positioned to serve as boundary actors. Although our results suggest these individuals can be effective in sharing and translating key knowledge, supporting transfers of benefits, and enabling or negotiating entitlements, we also found that social, political, institutional, and geographic constraints limited their effectiveness. In particular, the Topnaar Traditional Authority, adopted a “neo-traditional,” top-down, gatekeeper role, while their community wanted them to be more responsive and engaged in directly addressing the communities’ problems. In general, the boundary actors were the focus of much discontent and conflict, in large part because of unclear pathways of accountability. We recommend the co-creation of boundary objects that specify responsibilities and thus reduce conflict and support effective boundary actors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".