Bibliographic record
Abstract
Having your apartment searched and your laptop computer and research notes confiscated by the Russian Federal Security Service (FSB) is an exciting but abysmal way to end a research year in Russia. I had been invited by the U.S. and Canada Institute of the Russian Academy of Sciences to spend 10 months there in 1999 doing research for my Ph.D. thesis. It wasn’t my first visit: during most of the 1990s I had worked for Greenpeace, lobbying in Moscow and Washington for further nuclear disarmament. As an academic, I was studying why the arms control process had stalled in the mid-1990s. After all, with the Cold War over, greater progress could have been expected. But in both capitals, nationalist politicking had thrown dangerous amounts of sand into the wheels of the arms control process. Indeed, by 1999, strategic arms control seemed headed for a major train wreck.
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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.052 | 0.038 |
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".