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Making School Math Messy: Deepening Mathematical Appreciation in Gifted High School Students

2007· article· en· W2562849954 on OpenAlex
Laura McMaster, Paul Betts

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGifted and Talented International · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsUniversity of WinnipegCargill (Canada)
FundersLeibniz-GemeinschaftMcMaster University
KeywordsMathematics educationVisionPhilosophy of mathematics educationReform mathematicsMath warsMemorizationPedagogyConnected MathematicsMathematicsPsychologySociology

Abstract

fetched live from OpenAlex

The literature and our experiences suggest that gifted students believe doing mathematics is fi nding the right answer and learning mathematics involves memorizing isolated procedures. These beliefs are asynchronous with reform efforts predicated by a socio-cultural view of the teaching of mathematics and with the discipline of mathematics described within the philosophy of mathematics literature. We developed a philosophy of mathematics unit based on a notion of ’messiness’ and implemented it with gifted high school students during a philosophy course. Messiness highlights the uncertain, social, and contextual aspects of school mathematics. Preliminary analyses suggest that while most students did not engage with alternative visions of mathematics, some did, and their appreciation of mathematics seemed to grow. We conclude that high school math for all gifted students, not just those taking philosophy, should be infused with messiness.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.396
Teacher spread0.363 · 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