Bibliographic record
Abstract
Abstract The United Nations (UN) has more experience than any other organization in the world as a third-party uniformed presence on the ground seeking peace in conflict zones. This task requires large amounts of intelligence to stay safe and implement multidimensional mandates like humanitarian assistance, ceasefire monitoring, mediation between warring parties, protection of civilians, verification of elections and peace agreements, peacebuilding, peace enforcement, and many other tasks assigned by the UN Security Council. After decades of unwisely shunning intelligence, the UN finally realized in the 2010s that it needed to formally accept the practice of peacekeeping-intelligence (PKI)—that is, multisource information gathering and analysis specifically to assist UN missions in conflict zones. Since 2017, the world organization has been creating PKI policies, doctrine, handbooks, and courses. It benefitted from a long history of successes and failures in early warning, fact-finding, sanctions monitoring, use of informants, and information gathering generally. The lessons are still being learned, so case studies remain crucial. The soldiers, police, and civilians on the ground have new structures and mechanisms to gather, collate, analyze, and disseminate critical information to save lives and alleviate human suffering.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.054 |
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".