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Record W2193863684

A Retrospective-Comparative Evaluation of Textbooks Developed by Native and Non-native English Speakers

2012· article· en· W2193863684 on OpenAlexvenueno aff
Javad Gholami, Farahnaz Rimani Nikou, Arya Soultanpour

Bibliographic record

VenueJournal of academic and applied studies · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistFirst languageMathematics educationLinguisticsComputer sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

The aim of this study was to evaluate the suitability of the textbooks World English 1, as a book written by native speakers of English, and ILI 1, as a book written by non native speakers of English for elementary level students based on two criteria (illustrations, and physical make-up) adopted from Doaud & CelceMurcia’s (1979) checklist in a comparative way. The study was conducted at Atlas Language Institute in Urmia and Iran language Institute (ILI) in Urmia. The participants of the study were 120 and 100 students (50 in each institute) and 20 teachers (10 in each institute). Then, the obtained data were analyzed by calculating the level of meaningfulness, mean score, and T value by using SPSS software. The results of the study revealed that illustrations and physical make-up which students rated them as having almost equal suitability were not rated as the same by teachers; since teachers, resorting their experiences and profession, believe that native written book is better than nonnative written one.

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)0.010
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.116
GPT teacher head0.355
Teacher spread0.239 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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