MétaCan
Menu
Back to cohort
Record W4412156013 · doi:10.1038/s41597-025-05453-3

Wave 2 of the Multilingual Eye-Movement Corpus (MECO): New text reading data across languages

2025· article· en· W4412156013 on OpenAlexafffund
Noam Siegelman, Sascha Schroeder, Yaqian Bao, Cengiz Acartürk, Niket Agrawal, Lena Sophia Bolliger, Jan Brasser, César Campos-Rojas, Denis Drieghe, Dušica Filipović Đurđević, Sofya Goldina, Romualdo Ibáñez, Lena A. Jäger, Ómar I. Jóhannesson, Anurag Khare, Nik Kharlamov, Hanne Bruun Søndergaard Knudsen, Árni Kristjánsson, Charlotte E. Lee, Jun Ren Lee, Márcia Eduarda Cruz Leite, Simona Mancini, Nataša Mihajlović, Ksenija Mišić, М. В. Орехова, Olga Parshina, Milica Popović Stijačić, Athanassios Protopapas, David R. Reich, Anurag Rimzhim, Rui Rothe‐Neves, Thaís Maíra Machado de Sá, Andrea Santana Covarrubias, Irina A. Sekerina, Heida Maria Sigurdardottir, Anna G. Smirnova, Priyanka Srivastava, Elisângela Nogueira Teixeira, Ivana Ugrinic, Kerem Alp Usal, Karolina Vakulya, Ark Verma, João Marcos Munguba Vieira, Denise H. Wu, Jin Xue, Sunčica Zdravković, Junjing Zhuo, Laoura Ziaka, Victor Kuperman

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaAgencia Nacional de Investigación y DesarrolloDet Obelske FamiliefondMinistry of Education, IndiaAzrieli FoundationMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaNorges ForskningsrådIsrael Science FoundationConselho Nacional de Desenvolvimento Científico e TecnológicoAalborg UniversitetBundesministerium für Bildung und ForschungNational Research University Higher School of EconomicsCanada Research ChairsGovernment of Canada
KeywordsReading (process)Computer scienceEye movementMovement (music)Natural language processingLinguisticsArtificial intelligenceArt

Abstract

fetched live from OpenAlex

This paper reports the Wave 2 expansion of the Multilingual Eye-Movement Corpus (MECO), a collaborative multi-lab project collecting eye-tracking data on text reading in a variety of languages. The present expansion comes with new eye-tracking data of N = 654 from 13 languages, collected in 16 labs over 15 countries, including in several languages that have little to no representation in current eye-tracking studies on reading. MECO also contains demographic, language use, and other individual differences data. This paper makes available the first-language reading data of MECO Wave 2 and incorporates reliability estimates of all tests at the participant and item level, as well as other methods of data validation. It also reports the descriptive statistics on all languages, including comparisons with prior similar data, and outlines directions for potential reuse.

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 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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.446
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.004
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 head0.085
GPT teacher head0.423
Teacher spread0.339 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations7
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueScientific DataSame topicReading and Literacy DevelopmentFrench-language works237,207