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Record W7097468103

Differential Bundle Functioning on Mathematics and Science Achievement Tests: A Small Step Toward Understanding Differential Performance

2000· article· en· W7097468103 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsGrading (engineering)Test (biology)Student achievementAchievement testStandardized testSelection (genetic algorithm)Academic achievementSubject (documents)
DOInot available

Abstract

fetched live from OpenAlex

Almost all of the provinces and territories in Canada have an educational assessment program. These assessment programs are used to evaluate student performance on well-defined learning objectives put forth by the educational departments or ministries in each province. Alberta Learning, for example, measures student achievement in Grade 3, 6, and 9 in the content areas of Language Arts, Mathematics, Science, and Social Studies. Teachers are encouraged to include these scores for grading students and school results are frequently reported in local newspapers. In some cases, these tests account for a substantial percentage of a student’s final course Grade. In British Columbia, the Grade 12 provincial examinations include over 19 subject areas (the highest found in Canada) with the exams being worth 40 % of the student’s final course grade. In Alberta, diploma examinations are administered in 11 subject areas at the Grade 12 level and account for 50 % of the student’s final course grade. The results of these tests are used to ensure that the province-wide standards are being met (Lafleur & Ireland, 1999). Universities also use these provincial exam scores for selection purposes. It is here where a single test score can have an important effect on a student since these scores contribute to the selection decisions at many universities. Consequently, test developers must ensure that their examinations are fair for all students.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.075
GPT teacher head0.309
Teacher spread0.234 · 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.

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

Citations0
Published2000
Admission routes1
Has abstractyes

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