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Теоретические основы и практика оценивания качества общего образования в средней школе Канады

2025· article· ru· W4417174735 on OpenAlexaboutno aff
Ольга Сергеевна Беликова

Bibliographic record

VenueSocialʹnaâ kompetentnostʹ. · 2025
Typearticle
Languageru
FieldDecision Sciences
TopicEducational Assessment and Improvement
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Quality assessmentStress (linguistics)Reliability (semiconductor)Foundation (evidence)Educational assessment

Abstract

fetched live from OpenAlex

В статье рассматриваются основные тенденции в оценивании качества общего среднего образования в Канаде. Автор выявляет как теоретические основы и предпосылки для оценивания, так и их практическое применение. Определяются такие направления в оценивании качества школьного образования в Канаде, как усиленное внимание к вопросам валидности и надёжности тестов, опора на результаты учебных достижений школьников, акцент на самооценку и саморегуляцию школьных систем, взаимосвязь оценки качества школьного образования и его дальнейшего улучшения. The main trends in the assessment of the general secondary education quality in Canada are considered in the article. The author investigates both theoretical foundation and the background of the assessment, and its practical application. Such trends as close attention to the issues of validity and reliability of tests, the emphasis on the educational achievements results, the accent on the self-assessment and self-regulation of the school systems, the interrelation of the school education quality assessment and its further improvement are defined in the assessment of school education quality in Canada.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.006
Scholarly communication0.0100.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.004

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 head0.060
GPT teacher head0.448
Teacher spread0.388 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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