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Record W2337150116 · doi:10.1177/0265532214559115

Design in four diagnostic language assessments

2015· article· en· W2337150116 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

VenueLanguage Testing · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLanguage assessmentLanguage proficiencyPsychologyEducational assessmentProcess (computing)Intervention (counseling)Educational researchMathematics educationManagement scienceComputer sciencePedagogy

Abstract

fetched live from OpenAlex

The studies documented in the four articles in this special issue uniquely exemplify principles of design-based research as follows: by taking innovative approaches to significant problems in the contexts of real educational practices; by addressing fundamental pedagogical and policy issues related to language, learning, and teaching; and, in the process, by refining their claims and assessment systems. I analyze and compare the four studies in view of Anderson and Shattuck’s (2012) guiding principles of design-based research: real educational contexts, design and testing of a significant intervention, mixed research methods, multiple iterations, collaborative partnerships, and practical impact on educational practices. The four studies differ in numerous respects but are mutually informative about conducting systematic inquiry into diagnostic language assessments. The focus of their analyses on distinct aspects of language and communication relevant to particular educational programs and populations suggest that diagnostic language assessments tend more toward specific purposes assessment rather than general language proficiency testing.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.109
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.217
GPT teacher head0.457
Teacher spread0.240 · 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