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Study of the Characteristic Mental Development Features of Primary School Students with Visçion Impairments

2021· article· en· W3210250120 on OpenAlexvenueno aff
Ievgeniia Synova, Valentyna Tarasun, Iryna Sasina, Tetyana Grebeniuk, Kateryna Glushenko

Bibliographic record

VenueJournal of Intellectual Disability - Diagnosis and Treatment · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionAction (physics)ObstacleVisual impairmentCognitive impairmentLow visionCognitive psychologyDevelopmental psychologyPsychiatryOptometryMedicine

Abstract

fetched live from OpenAlex

Severe vision impairments are an obstacle to the adequate cognitive and social development of the child. The educational response to priority problems that occur with vision disorders requires appropriate training of vision impairment specialists. For this purpose, they need basic knowledge related to the classification of vision impairments and the main aspects of the development and education of this category of children. This study aimed to analyse the main features of educational activities of children with vision impairments using special diagnostic methods and to search for effective methods for correcting the cognitive activity of children with vision impairments. During the study, an experiment was conducted with children with vision impairments (15 children). To conduct the experiment, the study used methods proposed by V.V. Tarasun and adapted them to the contingent of children with vision impairments. In particular, the following methods: the method of motivational preferences "Three wishes", the method of "Memorising 10 words", and the method of "What, why, how". The depth and time of vision impairment are considered as a primary defect, which has corresponding secondary consequences and requires corrective action under the guidance of an experienced vision impairment specialist.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.153
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.057
GPT teacher head0.303
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 teacher head, 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

Citations0
Published2021
Admission routes1
Has abstractyes

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