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Record W1976267793 · doi:10.1177/0013164403258444

Gender and Language Differences on the Test of Workplace Essential Skills: Using Overall Mean Scores and Item-Level Differential Item Functioning Analyses

2004· article· en· W1976267793 on OpenAlexaff
Theresa J. B. Kline

Bibliographic record

VenueEducational and Psychological Measurement · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyDifferential item functioningNumeracyTest (biology)Reading (process)Developmental psychologyItem response theoryPsychometricsLiteracyPedagogy

Abstract

fetched live from OpenAlex

The Test of Workplace Essential Skills (TOWES) assesses cognitive skills in three areas using the following three separate subscales: Reading Text, Document Use, and Numeracy inWorking-Age Adults. The sample was composed of 2,688 working-age Englishspeaking Canadians who came from a variety of settings (e.g., trades training programs, adult education centers, college programs, and athletic clubs). The relationships between subscale test performance and the demographic variables of gender and language showed there were some group differences in mean levels of performance. However, at most, these differences accounted for less than 3% of the variance in performance on any subscale. In addition, differential item functioning analyses using the BILOG-MG program showed that at the item level, little or no gender or language bias was present. Recommendations based on the findings are presented.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
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 head0.297
GPT teacher head0.409
Teacher spread0.113 · 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

Citations9
Published2004
Admission routes1
Has abstractyes

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