Conference of American Armies : countering threat networks.
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
The Conference of American Armies (CAA) is a military organization made up and led by armies from the American continents with the authorization of their respective countries. Currently, there are 22 member armies, including: Antigua & Barbuda, Argentina, Barbados, Bolivia, Brazil, Canada, Chile, Colombia, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Jamaica, Mexico, Nicaragua, Paraguay, Peru, Trinidad & Tobago, United States, Uruguay, and Venezuela. Additionally, there are four observer armies, including: Belize, Guyana, Suriname, and Spain.\n\nThe purpose of this event was to fulfill the overall goals approved by the CAA Army Commanders during the CAA Commanders Conference hosted by General (GEN) Mark A. Milley, U.S. Army Chief of Staff, in Washington D.C., in November 2017, which included: Conduct a realistic threat assessment to determine how the CAA member armies can provide military support to civil authorities to confront transregional, transnational threat networks (drug trafficking, terrorism, transnational organized crime, arms trafficking, and human trafficking). Share tactics, techniques, and procedures in operations, training, and exercises to strengthen leadership, readiness, and interoperability among military and civilian security forces in the region.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it