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Record W3175254360 · doi:10.20360/langandlit29551

Reimagining Numeracies: Empowered, Game-Informed Meaning Making in and beyond the Pandemic Era

2021· article· en· W3175254360 on OpenAlexvenueno aff
Sandra Schamroth Abrams

Bibliographic record

VenueLanguage and Literacy · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)PandemicSociologyMeaning-makingLiteracyOrder (exchange)Value (mathematics)Coronavirus disease 2019 (COVID-19)PedagogyPsychologyPublic relationsMedia studiesPolitical scienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

This article focuses on how a game-informed culture in public school math classes sustained interaction, cooperation, and empowered meaning making when COVID-19 mandates closed school buildings and education went fully online. More specifically, the game-informed learning environment supported the students’ development and discussion of their multimodal numeracies, and the highlighted activity reveals how the generation of math memes can foster students’ engagement in creative and empowered practices. Underscored throughout this article is the importance to embrace the expansiveness of numeracies in order to recognize, value, and support students’ meaning making.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.012
Scholarly communication0.0060.006
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.358
Teacher spread0.341 · 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 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

Citations8
Published2021
Admission routes1
Has abstractyes

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