Bibliographic record
Abstract
Abstract During the last quarter of a century, Uruguay has contributed more to UN peacekeeping operations than any other South American nation and was one of the top twenty countries in the ranking of the UN’s Department of Peacekeeping Operations (DPKO) between 2001 and 2016. This is striking when one bears in mind that Uruguay’s population is less than 3.5 million and that the size of its armed forces has been steadily reduced since 1985. With these credentials, Uruguay secured a non-permanent seat on the UN Security Council between 2016 and 2017, a position it had only previously held between 1965 and 1966. Contributing to peace operations has been a novelty in Uruguay’s foreign policy in the post-dictatorship era, though without breaking with the traditional principles of its foreign policy and strategic identity. Indeed, multilateralism and an adherence to the principles of non-intervention and negotiated conflict resolution have been consistent elements of Uruguayan foreign policy since the beginning of the 20th century. In fact, the motivations for Uruguay’s striking level of commitment to the UN peace operations are mainly linked to the evolution of civil–military relations after the dictatorship of 1973–1985.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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 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".