MétaCan
Menu
Back to cohort
Record W6969122908 · doi:10.5281/zenodo.6906357

THE PERCEPTION OF SPECIAL EDUCATION TEACHERS TOWARDS PUPILS WITH DOWN SYNDROME

2019· article· en· W6969122908 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsSpecial educationPerceptionDown syndromeQuarter (Canadian coin)Special needsDescriptive statisticsQuality (philosophy)

Abstract

fetched live from OpenAlex

This study aimed to investigate the perception of special education teachers on the characteristics and problems of children with Down Syndrome. This study is a survey using a questionnaire which has 45 items administered to 50 teachers of special education. The study is adopting quantitative research concept. Data were analyzed using the Statistical Package for Social Sciences version 22.0 for descriptive window, which are to generate descriptive and inferential statistic. The finding have shown that learning a critical problem among children with Down Syndrome is a high level, exceeding the third quarter or 75 percent giving positive feedback. Overall, the finding clearly show that the positive feedback on the characteristics and problems of children with Down Syndrome. Also concluded that in order to teach children with Down Syndrome, a teacher needs to understand the characteristic and learning problems of students with Down syndrome before being able to improve the quality of special education services.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.266
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 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicChild Development and EducationFrench-language works237,207