Canada and Aichi Biodiversity Target 11: understanding ‘other effective area-based conservation measures’ in the context of the broader target
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A renewed global agenda to address biodiversity loss was sanctioned by adoption of the Strategic Plan for Biodiversity 2011–2020 and the 20 Aichi Biodiversity Targets in 2010 by Parties to the Convention on Biological Diversity. However, Aichi Biodiversity Target 11 contained a significant policy and reporting challenge, conceding that both protected areas (PAs) and ‘other effective area-based conservation measures’ (OEABCMs) could be used to meet national targets of protecting 17 and 10 % of terrestrial and marine areas, respectively. We report on a consensus-based approach used to (1) operationalize OEABCMs in the Canadian context and (2) develop a decision-screening tool to assess sites for inclusion in Canada’s Aichi Target 11 commitment. Participants in workshops determined that for OEABCMs to be effective, they must share a core set of traits with PAs, consistent with the intent of Target 11. (1) Criteria for inclusion of OEABCMs in the Target 11 commitment should be consistent with the overall intent of PAs, with the exception that they may be governed by regimes not previously recognized by reporting agencies. (2) These areas should have an expressed objective to conserve nature, be long-term, generate effective nature conservation outcomes, and have governance regimes that ensure effective management. A decision-screening tool was developed that can reduce the risk that areas with limited conservation value are included in national accounting. The findings are relevant to jurisdictions where the debate on what can count is distracting Parties to the Convention from reaching conservation goals.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it