meaning and practice of stewardship in South Africa
Bibliographic record
Abstract
Stewardship offers a means of addressing social-ecological sustainability challenges, from the local to the global level. The concept of stewardship has had various meanings attached to it over time, and the links between the theory and practice of stewardship are not well understood. We sought to characterise the practice of stewardship in South Africa, to better understand the relationship between theory and practice. We found that practitioners’ understandings of stewardship coalesce around two core notions: the idea of stewardship as ‘responsible use and care’ of nature, and stewardship as a ‘balancing act’ between stewards’ use of natural resources for agricultural production and their responsibility to protect and manage the wider ecosystem. Stewardship practice in South Africa is strongly influenced by the biodiversity stewardship tool; however, many practitioners are integrating biodiversity stewardship with other approaches. These emerging social-ecological stewardship initiatives operate at landscape-level and work towards integrated social and ecological stewardship outcomes, by facilitating collaboration among diverse stakeholders. Further research is needed to better understand what is required to support these integrated, collaborative and cross-sectoral initiatives. Policy mechanisms that facilitate integrated place-based stewardship practice can contribute to expanding the practice of biodiversity stewardship in South Africa. Significance: Our findings contribute to a growing understanding of what stewardship looks like in South Africa and how it is put into practice. We show that biodiversity stewardship is a prevalent understanding of stewardship practice in South Africa and is often combined with other approaches for sustainable landscape management. A broader understanding of stewardship, for example through the concept of social-ecological stewardship, can enable more integrated, collaborative approaches to landscape management, addressing the wide range of environmental and social development challenges faced in rural landscapes across South Africa.
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.
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 | 0.009 | 0.010 |
| 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 | 0.037 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".