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Record W2791192603 · doi:10.1177/0261429418757869

Accommodation practices for gifted students

2018· article· en· W2791192603 on OpenAlexaffabout
Pei-Ying Lin, Yu-Cheng Lin

Bibliographic record

VenueGifted Education International · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAccommodationPsychologyTest (biology)Context (archaeology)Scale (ratio)Standardized testLiteracyPedagogyMathematics educationGeography

Abstract

fetched live from OpenAlex

Previous studies on Indian education have urged educators to address serious concerns about inequality and inequity in gifted education, and about the assessments for students educated in extremely diverse social and cultural landscapes in India. Although it is believed that appropriate test accommodations ensure valid and meaningful test results, accommodation practices for gifted students are rarely examined worldwide. To narrow this research gap, the present study examined a large-scale provincial literacy assessment in Ontario, Canada, as a test case, to investigate the accommodations used by gifted students and teachers. In particular, we analyzed 3-year assessment data sets to track the patterns of accommodation practices over time. Furthermore, we discussed potential implications for future research and the development of assessments for assessing gifted Indian students while being mindful of diverse cultural and educational landscapes in the context of India.

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)0.011
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.495
Teacher spread0.428 · 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
Published2018
Admission routes2
Has abstractyes

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