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Record W4390999167 · doi:10.51644/9781771123822

Deportment

2018· book· en· W4390999167 on OpenAlexaboutno aff
Alessandro Porco, Alice Burdick

Bibliographic record

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

Abstract

fetched live from OpenAlex

Deportment is a selection of poems – surreal, cerebral, and defiant – by Alice Burdick. Burdick examines the dangers of dogma, women’s rights, and environmental degradation in biting satires, moving elegies, and anti-sentimental lyrics filled with mischievous wordplay. The selection includes some of Burdick’s most iconic poems as well as rare work from the beginning of her career in 1990s Toronto and previously unpublished material. Burdick’s later poetry, more expansive in form and subject matter, addresses motherhood, the rural landscape, and sex and desire at middle age. Deportment makes the case for Alice Burdick as one of Canada’s best poets, alongside figures such as Lisa Robertson, Karen Solie, and Sina Queyras. Alessandro Porco’s introduction situates Burdick’s early work within the Toronto small press scene, focusing on her fugitive chapbooks, broadsides, and literary ephemera while highlighting her formative relationships with Victor Coleman and Stuart Ross. He traces her move from Toronto to Nova Scotia in the early 2000s and the impact of publishing from the social and spatial margins of Canadian literature. In her afterword, Burdick reflects on everyday life – as a poet and citizen, daughter and mother –in both the zombieland of downtown Toronto and the alien geography of Eastern Canada. She explores how the comparative speed, sound, and density of urban and rural spaces have shaped her literary imagination.

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)0.003
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: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0270.007

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 head0.017
GPT teacher head0.206
Teacher spread0.190 · 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
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
Published2018
Admission routes1
Has abstractyes

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