Arranging Deckchairs on the Titanic:Climate Change, Greenhouse Gas Emissions and International Shipping
Bibliographic record
Abstract
In 1988 the World Meteorological Organisation (WMO) and the United Nations Environment Programme (UNEP) established the Intergovernmental Panel on Climate Change (IPCC) to assess ‘the scientific, technical and socioeconomic information relevant for the understanding of the risk of human-induced climate change. Subsequently, in 1992, the United Nations (UN) proposed a framework for dealing with climate change, the United Nations Framework Convention on Climate Change (UNFCCC). UNFCCC divides UN member states into developed (Annex I) and developing (non-Annex I) nations. In 1997, the Kyoto Protocol (KP) was agreed as a practical attempt to put the aspirations of UNFCCC into effect. GHG emissions reduction targets were only set for Annex I nations under the UNFCCC concept of ‘common but differentiated responsibilities’; this precludes allocation of national ownership of GHG emissions from activities of an international character, specifically international shipping and aviation. Article 2(2) of KP (KP2(2)) assigns responsibility for these to Annex I nations through the international the International Civil Aviation Organization (ICAO) and the International Maritime Organization (IMO). In this paper, I have endeavored to reconcile the implications of GHG emissions, climate change and international shipping with IMO’s mandate under KP.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".