“As Far as Possible and as Appropriate”: Implementing the Aichi Biodiversity Targets
Bibliographic record
Abstract
Abstract Past shortfalls to meet global biodiversity targets have simultaneously prompted questions about the relevance of global environmental conventions, and sparked renewed ambition, for example, in the form of the Aichi Biodiversity Targets. While progress toward the Aichi Targets through the Convention on Biological Diversity is well‐documented globally, less is known at the national level. We conducted a systematic content analysis of 154 documents to assess the nature and extent of national implementation of the Aichi Targets using Canada as a case study. Results indicate that most responses are aspirational, with only 28% of responses implemented. Implemented responses tend to be associated with targets with specified levels of ambition that emphasize biophysical values, or targets that are relatively straightforward to achieve in this context (e.g., knowledge capacity and awareness). In contrast, targets focused on equity, rights, or policy reform were associated with fewer actions. Implementation of this latter class of targets is arguably stalled not solely because of a lack of effective target design, but because of lack of fit within existing institutional commitments. This suggests that solutions—in terms of improving implementation—lie not only in overcoming known dilemmas of quantifiability, but also in fostering institutional transformation.
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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 | 0.045 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".