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Record W2105897558 · doi:10.1162/coli.2009.35.4.35402

From Annotator Agreement to Noise Models

2009· article· en· W2105897558 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

VenueComputational Linguistics · 2009
Typearticle
Languageen
FieldComputer Science
TopicRough Sets and Fuzzy Logic
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsBenchmarkingComputer scienceNoise (video)AgreementData setQuality (philosophy)Set (abstract data type)Resource (disambiguation)Data qualityData miningData scienceEconometricsArtificial intelligenceMathematicsLinguisticsPhysics

Abstract

fetched live from OpenAlex

This article discusses the transition from annotated data to a gold standard, that is, a subset that is sufficiently noise-free with high confidence. Unless appropriately reinterpreted, agreement coefficients do not indicate the quality of the data set as a benchmarking resource: High overall agreement is neither sufficient nor necessary to distill some amount of highly reliable data from the annotated material. A mathematical framework is developed that allows estimation of the noise level of the agreed subset of annotated data, which helps promote cautious benchmarking.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score0.483

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.274
Teacher spread0.245 · 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