Ecological restoration as objective, target, and tool in international biodiversity policy
Bibliographic record
Abstract
Ecological restoration has been mainstreamed in international biodiversity policies in the last five years.I analyze statements about restoration in three international policies: the Convention for Biodiversity Strategic Plan 2011-2020 and Aichi Biodiversity Targets, the Convention for Biodiversity Decision XI/16 on ecosystem restoration, and the European Union's Biodiversity Strategy to 2020.I argue that restoration functions at three different levels in these policies: as an objective, as a target, and as a tool.Because restoration appears at all three levels, the policies encourage counting all restoration activity as meeting the objectives of the policy regardless of the activity's actual effect on ecosystem services or biodiversity more broadly.Reaching a numerical target for a restored area may not necessarily support the overarching policy goals of maintaining Earth's biodiversity and supporting ecosystem services.
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".