Using systems thinking to inform natural resource governance
Bibliographic record
Abstract
Abstract We examined the decision‐making processes in transboundary conservation as found in two southern African case studies, the Greater Limpopo Transfrontier Conservation Area, and the Greater Mapungubwe Transfrontier Conservation Area. Using systems thinking to approach the model of transfrontier natural resource governance and management provided insights at the landscape scale, particularly regarding the absence of a physical institution that can be evaluated and consequently improved. The overall objective of the research project was to consult with all layers of decision and policy makers involved in order to: synthesize the current state of knowledge, identify the range of potential incremental effects of management responses on natural and human systems, and determine the range of values that drive decision‐making processes. The methodology involved analyses of semi‐structured interviews with community members, park officials and managers at various levels, local government officials, national policymakers and NGOs involved in the TFCAs; and scrutiny of relevant policies and treaty documents. A value system framework was developed as a result, and each dimension received an aggregated score at country level. The findings particularly in the governance and decision making sphere were analysed using Capability Maturity Model theory and the NATO Network Enabling Capability model theory. The main recommendations suggest improving involvement in decision‐making processes from the grass‐roots or community level, developing communities of practice between the different countries and core protected areas, prioritizing policy harmonization, and establishing a physical TFCA unit with dedicated staff over longer periods of time than the current brief rotational cycle.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".