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Record W4200257804 · doi:10.37119/ojs2021.v27i1.494

A Rationale for the Junior-Senior Secondary Mathematics Curriculum 2.0

2021· article· en· W4200257804 on OpenAlexaffvenue
Glen S. Aikenhead

Bibliographic record

Venuein education · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCurriculumMathematics educationRelevance (law)Variety (cybernetics)Class (philosophy)Connected MathematicsMath warsReform mathematicsPedagogyMathematicsSociologyPolitical scienceEpistemologyLawPhilosophy

Abstract

fetched live from OpenAlex

This paper proposes a rationale that supports a renewal of our predominantly 19th century curriculum for Grades 7–12, identified as Mathematics 1.0. It was originally established in the mid 1800s to prepare learners mostly from upper-class families to succeed in a post-industrial society. Today’s digital revolution has changed society remarkably, and the variety of learners has certainly broadened, but Mathematics 1.0 fundamentally remains the same Plato-based (Platonist) curriculum due to its social-political power, which is documented in the article. The major changes to society’s culture and the composition of learners have caused faults in Mathematics 1.0 (e.g., a relevance deficit). For the majority of learners, school mathematics has mostly become an obsolete, inequitable, and harmful rite-of-passage into adulthood, to varying degrees. A renewed curriculum, Mathematics 2.0, is rationalized and specific suggestions are offered. The minority of learners who successfully pursue mathematics to varying degrees would experience small changes in their new Mathematics 1.2. Keywords: school mathematics, humanistic, curriculum differentiation, relevance

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)0.012
metaresearch head score (Gemma)0.015
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: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0060.016
Scholarly communication0.0070.005
Open science0.0020.006
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0040.002

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 head0.037
GPT teacher head0.369
Teacher spread0.331 · 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
GenreOther

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
Published2021
Admission routes2
Has abstractyes

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