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Record W4398634801 · doi:10.7910/dvn/28118

ICEWS Dictionaries

2015· dataset· en· W4398634801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHarvard Dataverse · 2015
Typedataset
Languageen
FieldArts and Humanities
TopicLexicography and Language Studies
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1280.107

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.

Opus teacher head0.034
GPT teacher head0.238
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it