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Record W2059295110 · doi:10.1087/20110208

Casting a wide net: the Journal Impact Factor numerator

2011· article· en· W2059295110 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

VenueLearned Publishing · 2011
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsThomson Reuters (Canada)
Fundersnot available
KeywordsImpact factorComputer scienceCitationSearch engine indexingOrder (exchange)Information retrievalFactor (programming language)Library scienceOperations researchData scienceMathematicsPolitical scienceEconomicsLawProgramming language

Abstract

fetched live from OpenAlex

ABSTRACT All metrics published in the Journal Citation Reports™ are dependent on the complete and correct aggregation of citations to each journal title. Here, we explain how unique cited titles are created for Thomson Reuters indexing, and how variations and ambiguities in titles are collected in order to create the Journal Impact Factor numerator.

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)0.042
metaresearch head score (Gemma)0.181
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0420.181
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0210.099
Science and technology studies0.0010.000
Scholarly communication0.0370.009
Open science0.0050.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.780
GPT teacher head0.563
Teacher spread0.217 · 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