Knowledge production for target-based biodiversity governance
Bibliographic record
Abstract
Target-based governance holds the promise of accountability by measuring progress towards objectives set within global environmental agreements. This approach has been widely adopted at multiple scales of governance in conservation and sustainability sectors including by the Convention on Biological Diversity (CBD). Yet the implications of governing through targets have not been fully scrutinized for the ways that targets and their associated indicators shape the work by global conventions, including the activities pursued by state and non-state actors. We systematically reviewed, coded and assessed the peer-reviewed scientific literature produced in relation to the CBD Aichi Biodiversity Targets (ABTs) to demonstrate the types of scientific action that the Aichi targets have motivated, and assess how this work serves to reinforce particular visions of what conservation is and how it should be done. We find widespread support for target-based governance in the scientific literature. We also find uneven attention across the targets (e.g. mostly the protected areas target), to particular elements within specific targets (e.g. the area-based element of the protected areas target), measured by particular forms of knowledge (i.e. primarily biophysical data aggregated at a global scale). We argue that the uneven scientific action associated with the ABTs arises from the interplay among target elements that can be readily measured (e.g. by existing single-metric indicators) and long-standing institutional commitments (e.g. to protected areas expansion). With the Post-2020 Global Biodiversity Framework on the immediate horizon, there is a timely opportunity to reflect on the role of targets within the CBD and beyond.
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 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 | 0.000 | 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) | 0.001 | 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 teacher head, 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".