Failure-proof or failure-prone? The paradoxes of global biodiversity institutions
Bibliographic record
Abstract
Abstract The number of global environmental institutions has increased dramatically over the past decade. Yet environmental governance is widely seen as failing. Focusing on biodiversity politics, we argue that many key governance institutions, particularly those advancing market solutions, are themselves deeply implicated in this persistent failure. Drawing on the sociology of expertise, we show how two recently established institutions – the European Business and Nature Platform and the Network for Greening the Financial System – attempt to address the uncomfortable reality of biodiversity governance failures and the risks of their own future failures by creating a series of diversions to deflect attention and by displacing the focus of biodiversity governance from core issues to their own efforts to develop metrics. These dynamics render these institutions both ‘failure-proof’ and inherently ‘failure-prone’, ultimately reinforcing rather than resolving the problems they aim to address.
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 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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.033 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".