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Record W2950323690 · doi:10.1177/0142723719850955

On the inefficiency of negative feedback in Russian morphology L1 acquisition

2019· article· en· W2950323690 on OpenAlexafffund
Elena Kulinich, Phaedra Royle, Daniel Valois

Bibliographic record

VenueFirst Language · 2019
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalCentre for Research on Brain Language and Music
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyVerbInefficiencyRepetition (rhetorical device)Negative feedbackControl (management)AudiologyCorrective feedbackInterval (graph theory)Feedback controlComputer scienceMathematicsLinguisticsMedicineArtificial intelligenceMathematics education

Abstract

fetched live from OpenAlex

This study investigates negative feedback effects on inflectional morphology acquisition in Russian. In order to examine the effects of adult feedback on child error elimination and assess the lasting effect of feedback, a series of elicited tasks was conducted with 65 Russian children aged from 3 to 4 years. Twelve verbs which undergo overregularization in the non-past tense resulting from applying the yod /j/-pattern were used as stimuli. The experiment was repeated over four sessions with bi-weekly intervals between sessions 1, 2, 3 and a four-week interval between sessions 3 and 4. Four groups of participants were formed with three types of feedback (Correction, Clarification Question and Repetition), and a control group without feedback. No significant differences were observed between groups with different feedback types, or even without feedback. This finding supports the general hypothesis that negative feedback is not a strong driver of recovery from overregularization errors in verb acquisition.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.252
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

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

Citations1
Published2019
Admission routes2
Has abstractyes

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