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Record W288529131

Why Teach Mathematics to All Students

2001· article· en· W288529131 on OpenAlexaboutno aff
Brent Davis

Bibliographic record

Venuefor the learning of mathematics · 2001
Typearticle
Languageen
FieldPsychology
TopicSocial Representations and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsPresentation (obstetrics)Subject (documents)Mathematics educationPhilosophy of mathematics educationPoint (geometry)Subject matterEpistemologySociologyMathematicsPedagogyConnected MathematicsPhilosophyCurriculumComputer science
DOInot available

Abstract

fetched live from OpenAlex

The title of this article is borrowed from a panel presentation at a recent conference. [1] At the close of our annual meeting, four members of the Canadian mathematics education community were invited share their thoughts on the topic of why mathematics is taught. My contributions this discussion were similar the arguments I made some years ago in an article in this journal (Davis, 1995), in which I attempted bring enactivist thought (Varela, 1999; Varela, Thompson and Rosch, 1991) bear on the question of why we teach mathematics. Through the session, though, some inadequacies with that thinking were highlighted. In particular, the seemingly innocuous phrase to all students, tacked the end of the question Why teach mathematics?, occasioned considerable response at the conference around matters of changes formal education over the past century, Western tendencies toward cultural imperialism and popular assumptions concerning a transcendent mathematics. Further issues have been raised by Peter Huckstep (2000) in his recent contribution the expanding debate around rationales for teaching mathematics. Among other matters, Huckstep argues that the utility of mathematics remains as viable a basis for teaching the subject as it ever was. He further suggests that other rationales which are more grounded in psychological and sociological discourses, while worthy of discussion, are not as compelling as those that are built on an acknowledgment of the usefulness of the subject matter. While I agree with Huckstep on the former point, I think that I disagree on the latter. In any case, prompted by my conference presentation and Huckstep 's discussion, I find that I am no longer comfortable with many aspects of my earlier article on the issue, and so I offer this account. With regard that past piece, this one might be seen as part elaboration, part clarification and part abdication.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0260.010

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 head0.076
GPT teacher head0.428
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations13
Published2001
Admission routes1
Has abstractyes

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