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Record W4406813595 · doi:10.3765/elm.3.5822

Coloring disjunction in child Romanian

2025· article· en· W4406813595 on OpenAlexfundno aff
Adina Camelia Bleotu, Mara Panaitescu, Anton Benz, Andreea C. Nicolae, Gabriela Bîlbîie, Lyn Tieu

Bibliographic record

VenueExperiments in Linguistic Meaning · 2025
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaConnaught FundLeibniz-GemeinschaftUniversitatea din BucureștiDeutsche Forschungsgemeinschaft
KeywordsRomanianBusinessPolitical scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Romanian children have been shown to rarely interpret the complex disjunction sau…sau ‘either...or’ (as in sau trenul sau barca ‘either the train or the boat’) exclusively (that is, as ‘only one, not both’) in Truth Value Judgment Tasks. Instead, children often favor an inclusive interpretation (‘one or both’), or a conjunctive interpretation (‘both, not just one’) (Bleotu et al. 2023, 2024a). Such findings contrast with those from Romanian adults, who consistently interpret this disjunction exclusively. In this study, we investigate whether children interpret sau...sau more exclusively in a Coloring Book Task (CBT), given previous evidence that children’s performance is more adult-like in tasks involving coloring rather than in truth value judgment tasks. In line with this expectation, we observed an increase in the number of exclusive-responding children compared to previous findings for Romanian. However, it is important to highlight that most children still did not interpret the disjunction exclusively, indicating ongoing challenges with the interpretation of disjunction around the age of five years.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.016
GPT teacher head0.349
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2025
Admission routes1
Has abstractyes

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