Bibliographic record
Abstract
Abstract Conserving the Oceans: The Politics of Large Marine Protected Areas documents the efforts of activists and states to increase the pace and scale of global ocean protections, leading to a new global norm in ocean conservation of large marine protected areas (MPAs) exceeding 200,000 km2. Through an analysis of domestic political economies, the book explains how states have protected millions of square kilometers of ocean space while remaining highly responsive to the interests of businesses. It argues that states design environmental policies above all around two key features of a given space: (1) the composition of extractive versus non-extractive industry interests; and (2) the salience of various industry interests, defined as the degree to which businesses would suffer tangible and significant costs in response to new environmental regulations. Through an analysis of large MPA advocacy campaigns in Australia, Palau, and the US, this book demonstrates how the political economy of a given marine space shapes how governments align their environmental and economic goals, sometimes strengthening conservation but more often than not undermining it. While recognizing important global progress and growing ambition to conserve ocean ecosystems, Conserving the Oceans demonstrates that even ambitious large MPAs have so far not fundamentally challenged a neoliberal paradigm of environmentalism that has caused considerable ecological harm.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".