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Record W4411152835 · doi:10.5539/jel.v14n6p57

The Socio-Critical Perspective of Mathematical Modeling: A Proposal on Prostate Cancer

2025· article· en· W4411152835 on OpenAlexvenueno aff
Lília Cristina dos Santos Diniz Alves, Daniana de Costa, Julio Silva de Pontes, Luana Pereira Cardoso, Thiago Rafael da Silva Moura, Maria Liduína das Chagas, Silvério Sirotheau Corrêa Neto, José Leão de Luna

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Prostate cancerPsychologyCancerMathematics educationMedicineComputer scienceArtificial intelligenceInternal medicine

Abstract

fetched live from OpenAlex

In this study, we approach Mathematical Modeling from a socio-critical perspective. Our aim was to achieve reflection and criticality in discussions about prostate cancer in the context of the Blue November campaign, which started in November 2011, hapenning every year from then and is dedicated to raising awareness about prostate cancer. Since Brazillian culture is still imbued with sexual prejudices, we think it is important to have, in our schools, talks about the taboo related to prostate cancer and the diagnostic tests to check a man prostate condition, especially in the case of the rectal examination. To this end, we conducted a qualitative, empirical study with 30 eighth-grade elementary school students in a school located in the city of Salinópolis, state of Pará, northern region of Brazil. As results, we highlight interdisciplinary, social, health and education debates that addressed behavioral issues, awareness, information and statistical literacy through reading, discussion and analysis of the mathematical models involved in the unfolding of the proposal.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.370
Teacher spread0.355 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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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