Bibliographic record
Abstract
Low Leverage : Cases that meet at least one of the following criteria: Large Economy: Total GDP more than $100 billion (1995, current US$) ( Source : World Bank World Development Indicators (online: www.worldbank.org/data)) Major Oil Producer: Annual production of more than one million barrels of crude oil per day average (1995) ( Source : U.S. Energy Information Administration, “International Energy Annual” (online: http://www.eia.doe.gov/emeu/iea/)) Possession of/capacity to use nuclear weapons (1990–1995) Medium Leverage: Cases that meet none of the criteria for low leverage but meet at least one of the following criteria: Medium-Sized Economy: Total GDP between $50 billion and $100 billion (1995, current US$). Source : World Bank World Development Indicators (online: www.worldbank.org/data) Secondary Oil Producer: Annual production of 200,000 to one million barrels of crude oil per day average (1995) ( Source : U.S. Energy Information Administration, “International Energy Annual” (online: http://www.eia.doe.gov/emeu/iea/)) Competing Security Issues: Country where there exists a major security-related foreign-policy issue for the United States and/or the EU. Beneficiary of Black Knight Assistance: Country that receives significant bilateral aid (at least 1 percent of GDP), the overwhelming dominant share of which comes from a major power that is not the EU or the United States (1990–1995). A major power is defined as a high-income country (per capita GDP of $10,000 or higher) or a major military power (annual military spending in excess of $10 billion, 1990–1995) ( Source : “Correlates of War,” available at www.cow2.la.psu.edu). China, France, Japan, and Russia are considered potential Black Knights.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".