Shifting baselines: From austerity to additionality in the mangrove forest
Bibliographic record
Abstract
Across the field of biodiversity conservation, talk of the ‘finance gap’ for nature – the shortfall of money needed to meet global targets to halt extinction and ecological collapse – abounds. This paper asks how the finance gap, and its assumption of limited state capacity and funding, inform what solutions for ecosystem conservation and restoration are pursued. By analyzing a leading ‘nature-based solution’ – the mangrove forest – this paper examines how the consensus that government budgets are, and will be, insufficient for ecosystem restoration is foundational to the rationale and social license of carbon crediting projects. Putting the history of mangrove degradation into conversation with current efforts for mangrove restoration reveals how both degraded natures and degraded state capacity are rendered dependent on private finance for their restoration – an outlook Bigger and Nelson term ‘austerity natures’. Drawing from critical scholarship on filling ‘finance gaps’ left by state austerity, this paper puts the mangrove forest, and other efforts to make markets out of degraded ecosystems, into conversation with this broader reorientation of state capacity towards enticing private finance into funding societal objectives.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".