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Record W2909184575 · doi:10.22158/wjer.v6n1p22

The Politics of Mathematics: Just and Knowing Societies

2019· article· en· W2909184575 on OpenAlexaff
Sandra Baldwin, Vicki Squires

Bibliographic record

VenueWorld Journal of Educational Research · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of SaskatchewanNipawin Bible College
Fundersnot available
KeywordsDemocracyContext (archaeology)PoliticsIdeologyMathematicsSociologyPolitical scienceLawBiology

Abstract

fetched live from OpenAlex

<p><em>Educational leaders and researchers recognize that mathematics can be an effective tool in enabling substantial advances in many fields of science and technology. However,</em><em> </em><em>the role that mathematics can play in shaping and creating socio-political views of societies</em><em> </em><em>is not as well understood.</em><em> </em><em>Within the mathematics’ learning community there is little discussion connecting the unique role that mathematics can play in conceptualizing a democratic society even within the democratic societies where that learning happens.</em><em> </em><em>Building capacity for learning in context is a critical piece of any comprehensive program but it is sometimes difficult for leaders to agree on what that context should and does look like.</em><em> </em><em>There are multitudes of influences at play when educational systems create and enact curricula but in order to push through the stalemate that can exist with different ideologies, it is essential to understand that mathematics</em><em> </em><em>can be a conduit to</em><em> </em><em>improvements in</em><em> </em><em>political social justice as well as a gateway to developments in science and technology. Mathematics has the potential to be a tool to create, as well as an instrument of influence; the key is for leaders to understand how to do both.</em></p>

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
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 integrity0.0000.001
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 head0.186
GPT teacher head0.528
Teacher spread0.342 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

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