MétaCan
Menu
Back to cohort
Record W2910555369 · doi:10.4087/aycc3142

On the Interrelation of Peer Climate and School Performance in Mathematics: A German-Canadian-Israeli Comparison of 14-Year-Old School Students

2004· article· en· W2910555369 on OpenAlexaboutno aff
Klaus Boehnke, Anna-Katharina Pelkner, Jenny Kurman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsGermanMathematics educationComputer sciencePsychologyGeography

Abstract

fetched live from OpenAlex

Recent international comparisons of students' scholastic achievement have once again shown enormous worldwide differences in the abilities of youngsters to comprehend text and to solve mathematical problems.The Program for International Student Assessment (PISA) included predominantly countries from the Organization for Economic Cooperation and Development (OECD), among others Germany and Canada.Canada ranked in the top achievement group of the PISA study.The achievement of Germany's students emerged as being in the lowest quarter of all participating countries, and was considerably below the OECD average (Adams & Wu, 2002, Baumeit et al., 2001).These results created a public uproar in Germany, in panicular because they replicated findings of the Third International Mathematics and Science Study (TIMSS) that had been published some years before (Ma1tin et al., 2000;Mullis et al., 2000), and in which Germany, Canada, and Israel had participated.In that study, which was confined to the assessment of mathematics and physics abilities, German students also barely reached the average of all countries paiticipating in it.In this study Canada and Israel had a ranking in the middle group of all panicipating countries as well.

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 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)0.000
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 score0.101
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.034
GPT teacher head0.357
Teacher spread0.324 · 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 teacher head, 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

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

Same topicSchool Choice and PerformanceFrench-language works237,207