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Case Study of Teaching Meta Communicative Competence Issues in Learners with ID

2019· article· en· W2966721072 on OpenAlexvenueno aff
Saimkulova Sholpan, Chaklikova Assel, S.A. Оdanova, R. Rakhmatova, Kapessova Taniya

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2019
Typearticle
Languageen
FieldComputer Science
TopicHigher Education and Teaching Methods
Canadian institutionsnot available
FundersUniversitas Indonesia
KeywordsCommunicative competenceCommunicative language teachingPsychologyCompetence (human resources)Meta-analysisMathematics educationPedagogyLinguisticsLanguage educationMedicineSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

Nowadays, the fact of lack of knowledge of bilingualism due to the changed volume ratio in the teaching of writing is becoming more and more apparent, especially to learners with intellectual disabilities. The emergence of complex processes marks this fact is interference and convergence of speech, which are sources of better speaking and writing in bilingualism. The purpose of the study is to identify the specific difficulties of teaching writing to younger bilingual schoolchildren with ID and to develop a productive speech therapy system for the prevention and correction of violations of higher mental functions that they have. The object of research is the features of teaching children with speech deficiency and with ID to writing in bilingual conditions. The subject of the research is the process of preventing and overcoming problems in the bilingual conditions of teaching children with speech and ID functions deficiency.

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)0.003
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.098
GPT teacher head0.382
Teacher spread0.284 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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