Transparency and Non-State Actors in the Regulation of Black Carbon Emissions from Arctic Shipping
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As Arctic sea ice recedes due to global warming, ship traffic is increasing, posing global climate risks, particularly from black carbon emissions. Emitted by ships burning heavy fuel oil, black carbon accelerates ice melt and contributes to climate change. Despite this urgency regulatory progress on the topic has been slow. The International Maritime Organization has debated Arctic black carbon emissions for over a decade with little advancement. Notably, regulatory efforts on the topic so far have been driven mainly by non-state actors rather than states. However, their regulatory influence is hindered by a critical barrier: a lack of transparency. This article analyses the crucial role of transparency in international law-making, specifically for non-state actors, using Arctic black carbon regulation as a case study. Drawing on semi-structured interviews, the article identifies transparency challenges and suggests recommendations to overcome them, thereby strengthening the role of non-state actors within the regulation.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it