Bibliographic record
Abstract
The Proliferation Security Initiative (PSI), launched by US President Bush in May 2003, is intended to prevent traffic in elements of weapons of mass destruction (WMD). Most WMD traffic moves by sea, and the focus of the PSI is on maritime interdictions and seizures. Although the PSI has had some significant successes, it has been criticised for lacking sufficient public accountability, stretching international law to the limits, undermining the UN system, potentially limited effectiveness and being politically divisive. Moreover, Asian countries that are key to PSI's successful implementation – notably China, India, Indonesia and South Korea – have deferred active involvement despite US pressure. Options for increasing PSI participation and enhancing its effectiveness include changes to existing international law; expanding existing conventions or developing a new one; obtaining an unambiguous empowering UN Security Council Resolution; obtaining NATO endorsement; arguing pre-emptive self-defence; and building a coalition of countries willing to perform such interdictions on each other's ships and aircraft on or over their territorial seas. However, each of these options would face obstacles and limitations that must be overcome for the PSI to be fully effective.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.146 | 0.033 |
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".