Other Effective Area-Based Conservation Measures, Biodiversity Stewardship and Statutory Intervention – A South African Perspective
Bibliographic record
Abstract
Area-based approaches are a central component of global efforts to conserve biodiversity. While the focus of many countries has been mainly on protected areas, other effective area-based conservation measures (OECMS) have been accorded global recognition in the past decade as a vital complementary approach to protected areas. This recognition has been reemphasised in the Kunming-Montreal Global Biodiversity Framework adopted by parties to the Convention on Biological Diversity in December 2022, with its Target 3 ratchetting up area-based coverage targets to 30 per cent by 2030. A growing focus and reliance on OECMs to contribute towards achieving this target is anticipated. The international community has in the past few years introduced some guidance to identify, secure, manage, monitor and verify the anticipated long-term biodiversity conservation outcomes of OECMs. Some commentators have argued for domestic legal intervention to complement this general international guidance. The South African Government has recognised the potential contribution of OECMs towards the achievement of domestic and global area-based biodiversity targets in its National Protected Areas Expansion Strategy (2018) but has alluded to the need for legal intervention to ensure that they achieve positive and sustained long-term outcomes for the in situ conservation of biodiversity. Some domestic commentators have highlighted the strong link between biodiversity stewardship (particularly conservation areas) and OECMs, advocating that these conservation areas should form the priority focus of domestic efforts to identify OECMs. This article scopes this potential link and specifically considers whether the current domestic legal and policy framework applicable to these conservation areas is sufficiently robust to ensure that only appropriate areas are identified as OECMs and that once recognised, they are governed and effectively managed in the long term. It highlights several frailties of the existing framework and drawing from anticipated legal reform in the Western Cape relating to biodiversity stewardship, it proposes a possible model for future national legislation regulating OECMs.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".