How Social Connections to Local CBNRM Institutions Shape Interaction: A Mixed Methods Case from Namibia
Bibliographic record
Abstract
Strong social connections between communities and institutions are essential to effective community-based natural resource management. Connectivity and willingness to engage with actors across scales are related to one’s perceptions of institutions managing natural resources. To better understand how individuals’ perceptions are related to connections between communities and institutions, and how these promote or inhibit interaction across scales, we carried out a mixed methods case study on the multiple actors living and working in the Namib Naukluft National Park in Namibia. We took a descriptive approach to the social network analysis and identified distinct subgroups as well as boundary actors for the community-institutional network. Thereafter, we regressed interview data on connections, perceptions, and willingness to reach out to institutions to understand more about network dynamics. Finally, we performed a qualitative analysis of interview data, to further highlight why community individuals were connected to institutional members. Positive perceptions are associated with greater connectivity for two out of three institutions. Better quality connections between community members and institutions was equated with a greater willingness (of community members) to reach out to an institutional member in only one out of three cases. As in other studies, willingness to reach out may be more strongly correlated to intergroup actor dynamics, as shown by subgrouping in the social network analysis, than one’s perceptions alone. This research highlights that direct interactions between community members and local institutions has the potential to support collaboration in the context of community-based natural resource management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".