Cultural and empowerment priorities amid tensions in knowledge systems and resource allocation: insights from the Great Limpopo Transfrontier Conservation Area
Bibliographic record
Abstract
Transfrontier Conservation Areas (TFCAs) are promoted as models of community-inclusive conservation, yet they often face criticism for inadequately incorporating community concerns into policy development. This study investigates community perspectives within the Great Limpopo Transfrontier Conservation Area (GLTFCA) using Q-methodology to explore diverse viewpoints on Park Management Plans (PMPs). The research addresses three primary questions: which aspects of the PMP are most valued by GLTFCA communities; the extent of agreement and disagreement among these communities; and the areas where community viewpoints show the most significant tensions. Data was collected through Q-sort exercises with 103 participants from four GLTFCA communities, followed by post-sorting interviews to enhance validity. Findings reveal that empowerment and cultural heritage are the most dominant aspects valued by the communities. There is a strong preference for direct, tangible benefits over long-term promises. However, significant tensions exist between traditional knowledge systems and modern resource allocation strategies, highlighting the complexities of integrating diverse perspectives into park management. The study's implications suggest that TFCA policies need to better align management strategies with community priorities, emphasizing tangible benefits and cultural heritage to enhance community engagement. Additionally, addressing epistemological diversity by providing diverse approaches linking to the pluralism of viewpoints or identifying other synergistic, transdisciplinary, and separation approaches can help manage these tensions. This study confirms the critical issues of benefit sharing within TFCAs, providing insights that can inform equitable management strategies through direct democratic processes. Additionally, it highlights the intra- and inter-community heterogeneity of perspectives, contributing to broader debates on the effectiveness of TFCAs.
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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 | 0.006 | 0.006 |
| 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 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".