Bringing the integrative aspect of sustainable development into community natura l resource management: the case of agricultural land use in limpopo, south africa
Bibliographic record
Abstract
Rural communities in South Africa manage natural resources under conditions of resource degradation driven by unsustainable practices.This is against a backdrop of the country adopting the principle of sustainable development and putting in place policies to facilitate integrated decision making, which is pivotal to sustainable development.Sustainable development is an integrative concept with a basis in a 'whole systems approach'.There are no tools tailored to facilitate integration in community level decision making in South Africa and there is need to develop such tools.In line with the stakeholder approach, users have to be involved in the development of the tools and inform their content.The question is whether community level users are able to adequately inform such tools.The objective of this study was to assess the input of potential community level users into development of a decision support tool for improving integration in natural resource management (specifi cally agricultural land use) decision making.Stakeholder analysis was used to identify decision makers and their responsibilities and elucidate decision-making processes, criteria, context and characteristics of the tool.The main fi ndings were that (i) community agricultural land use decisions focus on addressing social and economic needs with no consideration for the environment; (ii) users visualised the tool as a set of guidelines for enabling equal consideration of social, economic and environmental factors and expected it to facilitate group decision making, communication and participation of different stakeholders in decision making.Stakeholder expectations for the tool were different.Stakeholder analysis was used to accommodate these different perspectives and reach consensus on issues.Stakeholders were able to provide integral information to developing a tool that is both acceptable to users and addresses the integration principle of sustainable development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".