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Record W4390983677 · doi:10.51644/9781771124768-001

Foreword

2020· book-chapter· tl· W4390983677 on OpenAlexaboutno aff

Bibliographic record

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

Abstract

fetched live from OpenAlex

The Laurier Poetry Series was conceived in 2002 as a means to celebrate Canadian poetry and to introduce new readers to the richness and diversity of its poets.Rather than curate another large anthology that featured only a few poems by each poet, we thought it a better idea to suggest the real range of a poet's work by enlarging the selection.Our anthology would have to comprise many volumes.But why persist with a traditional anthology?Why not create a series of small and affordable volumes, each devoted to the work of a single poet?Each volume could be introduced by a knowledgeable reader, familiar with the poet's work and addressing it in greater depth than in normal anthologies; and each volume could close with the poet contributing an afterword such as no standard anthology could offer.Readers could pick and choose which poets they wanted to explore; instructors could also pick and choose combinations of volumes in a package for their students-and could change this selection from semester to semester.And the volumes could reach an international audience.Each would also have the potential to open out onto other books by the featured poet.That was the blueprint.The Series was launched in 2004, with Catherine Hunter's selection of the poetry of Lorna Crozier, Before the First Word.There have been over thirty volumes since, offering introductions to a wide range of poets and poetries, and more are in the works.The Laurier Poetry Series is now the most comprehensive collection of Canadian poetries in print anywhere.

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)0.008
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 score0.263
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.001
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.7370.725

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 head0.030
GPT teacher head0.213
Teacher spread0.184 · 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
Published2020
Admission routes1
Has abstractyes

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