Bibliographic record
Abstract
radually evolving from the embryonic detente initiatives of the 1970s, and having braved the Charybdian rocks of the still lingering Cold War of the 1980s, the Organization for Security and Cooperation in Europe (OSCE) finally emerged as a full-fledged international organization with the renaming in 1995 of what had previously been known as The Conference for Security and Cooperation in Europe (CSCE). On its website, the OSCE now boasts of being “the world’s largest regional security organization whose 55 participating States span the geographical area from Vancouver to Vladivostok.” The objectives of the OSCE are, broadly speaking, concerned with early warning, conflict prevention and post-conflict rehabilitation. Its listing of activities also includes such tasks as antitrafficking, arms control, border management, combating terrorism, conflict and democratization. The OSCE’s main tools in carrying out these tasks are its field operations. Acting under the directions from the OSCE Secretariat in Vienna, and under the general auspices of the organization’s Chairman-in-Council, the field operations comprise a number of rather diverse groups—each one with a specific mandate according to the problem(s) to be addressed in their respective operational areas. At the time of the writing (February 2008), the OSCE maintains 19 field operations in SouthEastern Europe, Eastern Europe, the Caucasus and Central Asia. These are the following:
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".