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Record W2182649006 · doi:10.36510/learnland.v4i1.357

Commentary: How and Why Does Poetry Matter? And What Do We Do About That?

2010· article· en· W2182649006 on OpenAlexaffvenue
Patrick Dias

Bibliographic record

VenueLEARNing Landscapes · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsMcGill University
Fundersnot available
KeywordsAntipathyPoetryDistrustCurriculumCompetence (human resources)PedagogySociologyOddsPsychologyLiteratureSocial psychologyArtLawPolitical scienceComputer science

Abstract

fetched live from OpenAlex

While there is a long and widely held belief that poetry matters and is a necessary component of the school curriculum, such convictions are at odds with the way poetry is taught and the general antipathy that students, especially in secondary school, hold towards it. Such disregard is well established among most school teachers who have been similarly schooled and consequently distrust their own competence as readers of poetry and unwittingly perpetuate such insecurity. Teachers need to act with some urgency to determine why poetry is such a valuable cultural and social good, and consider the easily accessible means by which poetry can be enthusiastically embraced by their pupils.

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)0.006
metaresearch head score (Gemma)0.055
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.056
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.055
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0060.008
Scholarly communication0.0040.006
Open science0.0070.003
Research integrity0.0560.056
Insufficient payload (model declined to judge)0.0100.011

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 head0.012
GPT teacher head0.245
Teacher spread0.233 · 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
GenreCommentary

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

Citations6
Published2010
Admission routes2
Has abstractyes

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