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Record W4392325350 · doi:10.51644/9781771121927-001

Foreword

2015· book-chapter· en· W4392325350 on OpenAlexaboutno aff
Phil Hall, Rob McLennan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Early in the twenty-first century, poetry in Canada-writing and publishing it, reading and thinking about it-finds itself in a strangely conflicted place.We have many strong poets continuing to produce exciting new work, and there is still a small audience for poetry; but increasingly, poetry is becoming a vulnerable art, for reasons that don't need to be rehearsed.But there are things to be done: we need more real engagement with our poets.There needs to be more access to their work in more venues-in classrooms, in the public arena, in the media-and there need to be more, and more different kinds, of publications that make the wide range of our contemporary poetry more widely available.The hope that animates this series from Wilfrid Laurier University Press is that these volumes help to create and sustain the larger readership that contemporary Canadian poetry so richly deserves.Like our fiction writers, our poets are much celebrated abroad; they should just as properly be better known at home.Our idea is to ask a critic (sometimes himself a poet) to select thirtyfive poems from across a poet's career; write an engaging, accessible introduction; and have the poet himself write an afterword.In this way, we think that the usual practice of teaching a poet through eight or twelve poems from an anthology is much improved upon; and readers in and out of classrooms will have more useful, engaging, and comprehensive introductions to a poet's work.

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)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.265
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0050.004
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.7350.731

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 head0.064
GPT teacher head0.238
Teacher spread0.174 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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