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Record W2771575265 · doi:10.36510/learnland.v3i1.321

Life-Long Readers of Poetry? Why Not?

2009· article· en· W2771575265 on OpenAlexaffvenue
Patrick Dias

Bibliographic record

VenueLEARNing Landscapes · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsPoetryMeaning (existential)LiteraturePsychologySociologyPedagogyMathematics educationLinguisticsPhilosophyArt

Abstract

fetched live from OpenAlex

I have come to believe that most students who have read and studied poetry in school and college do not go on to read and enjoy poetry in their adult lives because they do not expect to understand a poem of any complexity on their own. Unfortunately, years of conditioning into such dependence have also victimized teachers into functioning as guardians of the poem’s meaning. In an attempt to find out what students could realize from a poem without teacher direction, I discovered in studies at various levels of schooling that students working in small groups without any expectation that the teacher held and would provide "the one right meaning," generated rich and powerful readings of challenging poems far beyond any understanding that the teacher could have mediated with questions.

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)0.015
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 score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.013
Scholarly communication0.0110.017
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0120.004

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 head0.021
GPT teacher head0.236
Teacher spread0.215 · 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

Citations0
Published2009
Admission routes2
Has abstractyes

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