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Record W2885169382 · doi:10.1111/emip.12211

How Robust Are Cross‐Country Comparisons of PISA Scores to the Scaling Model Used?

2018· article· en· W2885169382 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

VenueEducational Measurement Issues and Practice · 2018
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCriticismRobustness (evolution)UnderpinningItem response theoryPsychologyTest (biology)Cross countryPsychometricsPolitical scienceDevelopmental psychologyEconomicsDemographic economicsEngineering

Abstract

fetched live from OpenAlex

Abstract The Programme for International Student Assessment (PISA) is an important international study of 15‐olds' knowledge and skills. New results are released every 3 years, and have a substantial impact upon education policy. Yet, despite its influence, the methodology underpinning PISA has received significant criticism. Much of this criticism has focused upon the psychometric scaling model used to create the proficiency scores. The aim of this article is to therefore investigate the robustness of cross‐country comparisons of PISA scores to subtle changes to the underlying scaling model used. This includes the specification of the item‐response model, whether the difficulty and discrimination of items are allowed to vary across countries (item‐by‐country interactions) and how test questions not reached by pupils are treated. Our key finding is that these technical choices make little substantive difference to the overall country‐level results.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.494

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.130
GPT teacher head0.379
Teacher spread0.249 · 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