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The Social Dimension of Language Testing

2024· other· en· W4390795033 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe TESOL Encyclopedia of English Language Teaching · 2024
Typeother
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLanguage assessmentMultilingualismLiteracyTest (biology)Dimension (graph theory)On LanguageStandardized testPsychologyDifferential item functioningMathematics educationPedagogyLinguisticsPsychometricsItem response theoryDevelopmental psychology

Abstract

fetched live from OpenAlex

Language tests have high social power because they are used to inform important decisions that impact test takers' lives, related to education, employment, or citizenship. Unlike the traditional approaches of language testing that focus on psychometrics, critical language testing (CLT) focuses on the social dimension and the impact of language tests on test takers, education, and society. CLT proposes various effective language assessment approaches and strategies, such as dynamic assessment , literacy assessment , test accommodations and differential item functioning (DIF), alternative assessment , and multilingual assessments, which can reduce the power of language tests. The goal of CLT is not to eliminate language tests, but to uncover the hidden agendas behind them and to make them fair and inclusive. CLT aims to prevent test takers' discrimination and marginalization and to transform language tests into ethical educational tools that acknowledge the importance of multilingualism and diversity.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.125
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.021
GPT teacher head0.357
Teacher spread0.336 · 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