Collaborations with the International Science and Technology Center and the Science and Technology Center in Ukraine
Bibliographic record
Abstract
The International Science and Technology Center and the Science and Technology Center in Ukraine (STCU) are non-governmental organizations whose mandates are to foster the goals of international nonproliferation by distributing donor funds from the United States, Canada, and the European Union to former Soviet Union (FSU) scientists to work on non-defense related research projects. To that end, the ISTC and STCU provides support to scientists working on conversion projects, many of which relate to research and development to improve public health and the environment, especially in the recipient countries. For example, the STCU organizes workshops to give scientists from Ukraine, Uzbekistan, and Georgia, the three FSU countries supported through the STCU, the opportunity to meet with international environmental colleagues. This paper discusses opportunities by the ISTC and STCU to encourage the exchange of information. This paper also describes the opportunities for former Soviet weapons scientists to become better integrated in the global environmental science community and to help find Western engineers and scientists with whom to collaborate in future ISTC and STCU proposals and projects.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".