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
It is hard to imagine a threat to international security or a tension within U.S. foreign policy that does not involve the imposition of economic sanctions. The United Nations Security Council has fourteen sanctions regimes currently in place, and all member states of the United Nations are obligated to participate in their enforcement. The United States has some thirty sanctions programs, which target a range of countries, companies, organizations, and individuals, and many of these are autonomous sanctions that are independent of the measures required by the United Nations. Australia, Canada, the European Union, Japan, South Korea, and others also have autonomous sanctions regimes, spanning a broad range of contexts and purpose. Most well-known are those concerning weapons proliferation, terrorism, and human rights violations; but sanctions are also imposed in such contexts as money laundering, corruption, and drug trafficking. States may also impose sanctions as a means to achieve foreign policy goals: to pressure a foreign state to bend to the sanctioner's will, to punish those who represent a threat to the sanctioner's economic or political interests, or to seek the end of a political regime toward which the sanctioner is hostile, to give but a few examples.
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 | 0.001 | 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) | 0.025 | 0.026 |
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