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Record W4283776514 · doi:10.1007/s10649-022-10161-5

Storylines in public news media about mathematics education and minoritized students

2022· article· en· W4283776514 on OpenAlexaff
Annica Andersson, Ulrika Ryan, Beth Herbel‐Eisenmann, Hilja L. Huru, David Wagner

Bibliographic record

VenueEducational Studies in Mathematics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsUniversity of New Brunswick
FundersNorges ForskningsrådNational Science Foundation
KeywordsMathematics educationNorwegianReform mathematicsConnected MathematicsPerceptionMath warsGratitudePedagogyMathematicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract Public media both reflects and shapes societal perceptions and attitudes. Teachers and others around students in mathematics classrooms have expectations for the students, projected with what appears in these media. We are most concerned about the expectations placed on students who are identified with minoritized groups—particularly students who are Indigenous or migrated to Norway. We investigate how minoritized group contexts and mathematics education appear together in Norwegian news media texts. Our analysis uses the notion of storylines to describe the expectations about minoritized groups that news media project. We found seven entangled storylines: “the majority language and culture are keys to learning and knowing mathematics,” “mathematics is language- and culture-neutral,” “minoritized groups’ mathematics achievements are linked to culture and gender,” “extraordinary measures are needed to teach students from minoritized groups mathematics,” “students from minoritized groups underachieve,” “students from minoritized groups put in extraordinary effort and time to learn mathematics,” and “minoritized mathematics students are motivated by gratitude.”

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.218
GPT teacher head0.466
Teacher spread0.248 · 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 designQualitative
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

Citations12
Published2022
Admission routes1
Has abstractyes

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