MétaCan
Menu
Back to cohort
Record W2020915099 · doi:10.1177/00238309080510010601

The Delay of Principle B Effect (DPBE) and its Absence in Some Languages

2008· article· en· W2020915099 on OpenAlexaff
Anna Maria Di Sciullo, Calixto Agüero-Bautista

Bibliographic record

VenueLanguage and Speech · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicSyntax, Semantics, Linguistic Variation
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsLinguisticsPhenomenonPronounQuantifier (linguistics)Antecedent (behavioral psychology)Scope (computer science)Minimalist programDependency (UML)MathematicsPsychologyComputer scienceSyntaxPhilosophyArtificial intelligenceSocial psychology

Abstract

fetched live from OpenAlex

The Delay of Principle B Effect (DPBE) has been discussed in various studies that show that children around age 5 seem to violate Principle B of Binding Theory (Chomsky, 1981, and related works), when the antecedent of the pronoun is a name, but not when the antecedent is a quantifier. The analysis we propose can explain the DPBE in languages of the Dutch-English type, and its exemption in languages with (dis)placed pronouns (clitics). In both types of languages, the phenomenon arises when children have to compare two alternative representations for equivalence. The principle that induces the comparison is different in both cases, however. The comparision of children speaking languages with pronouns occurring within the VP is induced by Grodzinsky and Reinhart's (1993) Rule I. However, the comparison of children in languages where the pronouns occur above the VP is induced by Scope Economy. In both cases the result is similar: the children take guesses in the process of interpreting the anaphoric dependency, thereby performing at chance level.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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 designQualitative
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

Citations16
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueLanguage and SpeechSame topicSyntax, Semantics, Linguistic VariationFrench-language works237,207