Other Effective Area-Based Conservation Measures (OECMs) in Australia: Key Considerations for Assessment and Implementation
Bibliographic record
Abstract
Other effective area-based conservation measures (OECMs) have been a feature of global biodiversity targets since 2010 (Aichi Targets, Kunming-Montreal Global Biodiversity Framework), although the concept has only relatively recently been formally defined. Although uptake has been limited to date, there is much interest in identifying OECMs to contribute to the target of protecting at least 30% of terrestrial, freshwater and ocean areas by 2030, in conjunction with protected areas. Australia has a long history of protected area development across public, private and Indigenous lands, but consideration of OECMs in policy has recently begun in that country. We review principles proposed by the Australian Government for OECMs in Australia and highlight where these deviate from global guidance or established Australian area-based policy. We examined various land use categories and conservation mechanisms to determine the likelihood of these categories/mechanisms meeting the OECM definition, with a particular focus on longevity of the mechanism to sustain biodiversity. We identified that the number of categories/mechanisms that would meet the OECM definition is relatively small. A number of potentially perverse outcomes in classifying an area as an OECM are highlighted in order to guide proactive policy and program design to prevent such outcomes occurring.
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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 | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".