Bibliographic record
Abstract
This book assesses the extent to which two specialized UN agencies – the International Maritime Organization (IMO) in London and the International Civil Aviation Organization (ICAO) in Montreal – have been able to regulate environmental pollution in the global commons. Since the Kyoto Protocol and its tasking of these two public International Organizations (IOs) in 1997 to regulate greenhouse gas emissions from the fast-growing international shipping and aviation sectors, they have struggled with the assignment even as the external pressure has mounted for them to act. David Deese examines why these two UN agencies have largely failed to execute their critical missions to date and explores the most promising emerging and feasible routes to control and reduce these emissions by other means. Drawing on a range of sources including interviews with key actors in the IMO and ICAO, as well as from industry and national governments, Deese looks at the multifaceted politics that drive these IOs and considers how this has delayed and frustrated the execution of their assigned climate mitigation missions. He also explains how the limitations of the IMO and ICAO are likely to be found to a degree in other UN specialized agencies and examines how lessons learned here will be helpful in understanding the operations of other IOs. The book will be of great interest to students and scholars of global governance and IOs, transport, and environment and climate change. It will also be a useful resource for industry and non-profit experts and public officials working in shipping and aviation regulation.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".