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Record W2063044111 · doi:10.5539/ells.v4n4p16

Stages in Language Acquisition: A Case Study

2014· article· en· W2063044111 on OpenAlexvenueno aff
Jamal Azmi Salim, Momammad Mehawesh

Bibliographic record

VenueEnglish Language and Literature Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsArabicTRACE (psycholinguistics)LinguisticsTranscription (linguistics)Language acquisitionComputer scienceSecond-language acquisitionPsychologyNatural language processing

Abstract

fetched live from OpenAlex

The main objective of this study is to trace the language development of the child Anwar from a Jordanian Arabic-speaking home, from her first vocal sounds to the first sentences. The present study which is based on “naturalistic observations” covers the acquisition of Arabic during her pre-school age. The study was carried out by maintaining the proper records of the child utterances in the form of a ‘diary’ and the traditional method of phonetic transcription was used to record utterances. It was found that she had acquired Arabic by the time she was five. The same year she was admitted to kindergarten. The whole study will be confined to different stages of language 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)0.002
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0070.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.373
Teacher spread0.354 · 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 designCase report
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

Citations45
Published2014
Admission routes1
Has abstractyes

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