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Record W3001286655 · doi:10.17605/osf.io/n9pmd

Inter-rater reliability for study selection of Spanish language articles using Google’s Neural Machine Translation (Google Translate)

2020· article· en· W3001286655 on OpenAlexaff
Jorge Andres Delgado-Ron, J. Martín López, Solange Núñez-González

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMachine translationSelection (genetic algorithm)Natural language processingComputer scienceReliability (semiconductor)Artificial intelligenceInter-rater reliabilityInformation retrievalMachine learningStatisticsMathematics

Abstract

fetched live from OpenAlex

The goal of our project is to to measure the reliability of Google Translate to assist Native English Non-Spanish speakers in screening articles published in Spanish in systematic reviews.

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 imitation

Not 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.

metaresearch head score (Codex)0.350
metaresearch head score (Gemma)0.618
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.801

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3500.618
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0130.008
Science and technology studies0.0020.004
Scholarly communication0.0040.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.316
Teacher spread0.283 · 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

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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