MétaCan
Menu
Back to cohort
Record W2963983429 · doi:10.29173/iasl7152

Rewiring Empathy: The Value of Multicultural Literature in the Classroom

2017· article· en· W2963983429 on OpenAlexvenueno aff
Heather J. Conrad

Bibliographic record

VenueIASL Annual Conference Proceedings · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyMulticulturalismReading (process)Value (mathematics)PoliticsPsychologyContemplationSociologyEnvironmental ethicsSocial psychologyPedagogyPolitical scienceEpistemologyLawComputer science

Abstract

fetched live from OpenAlex

Reading multicultural novels cultivates empathy for diverse people, cultures, and environments in ways that Internet use cannot. The act of reading fictional books has been shown to increase the capacity for empathy in the reader. Internet use, by contrast, has been shown to reduce students’ ability to remember, concentrate, and engage in the deep reading and contemplation activity that develops empathy. Empathy is vital to our global future. Hate crimes are increasing in the United States, United Kingdom, and elsewhere; worldwide, the number of political and climate-change refugees is increasing and the biodiversity of other species is declining. Addressing these problems requires an increase in human empathy and cooperation. Therefore multicultural books are vital to preparing students for our changing world. It is up to schools to discover, acquire, and prioritize multicultural books in the classroom.

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)0.010
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0130.017
Scholarly communication0.0200.015
Open science0.0020.019
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.027
GPT teacher head0.263
Teacher spread0.236 · 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 designNot applicable
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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIASL Annual Conference ProceedingsSame topicThemes in Literature AnalysisFrench-language works237,207