ICEWS Dictionaries
Bibliographic record
Abstract
The ICEWS dictionaries contain both named individuals or groups, known as actors, and generic individuals or groups, known as agents. Actors are known by a specific name, such as 'Free Syrian Army' or 'Goodluck Johnathan', while agents are known by a generic improper noun, such as 'insurgents' or 'students'. Both actors and agents have time-dependent affiliations with another actor (in the case of an individual being a member of an organization, for example), a country or other autonomous region, or with a general sector/role, such as 'Military' or 'Government'. Also included in the dictionaries are aliases that an actor or agent might be known by. In the case of actors, these are typically alternate spellings of a person's name, while for agents they are typically synonyms. Additional information about the ICEWS program can be found at http://www.icews.com/. Follow our Twitter handle for data updates and other news: @icews
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.103 | 0.180 |
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".