Frames on human wildlife relationships in protected landscapes: lessons from the Gonarezhou National Park, Zimbabwe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the main challenges in multiple-use landscapes such as protected conservation areas is the coexistence of local communities with wildlife. This coexistence has been framed recently as human-wildlife relationships and plays a pivotal role in the governance of protected areas where communities and wildlife are entangled in complex interactions. We apply the frame concept to uncover the competing and conflicting, as well as multifaceted values, ideas, perceptions, and experiences of local communities and park officials that shape human-wildlife relationships in the Gonarezhou National Park, Zimbabwe. To surface different frames on human-wildlife relationships, we applied the Q-methodology. For this study we opted for a theory-driven Q-set that focused on a deductive development of Q-statements from the literature. In contrast to other studies, which focus on selected aspects of human-wildlife relationships, we take a systemic approach and include various facets (e.g., institutions, tangible and intangible costs, empathy, wildlife value orientation). The Q-Method was applied to 149 community members as well as park officials and led to 10 diverging frames on human-wildlife relationships. The findings furthermore revealed on the one hand that park officials are not a homogenous group that can be clearly distinguished from the communities in their perceptions, values, and experiences, nor are the communities themselves homogenous. In contrast, we identified intra-and inter-community frame conflicts. The revealed variety of frames improves the understanding of conservation conflicts and supports more equitable governance of protected areas and landscapes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it