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Record W4313496715 · doi:10.12795/ren.2022.i26.15

POETRY IN PANDEMIC TIMES: MOURNING COLLECTIVE VULNERABILITY IN SUE GOYETTE’S SOLSTICE 2020. AN ARCHIVE.

2022· article· en· W4313496715 on OpenAlexaboutno aff
Leonor María Martínez Serrano

Bibliographic record

VenueRevista de Estudios Norteamericanos · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsPoetryVulnerability (computing)SolsticePandemicEvent (particle physics)Face (sociological concept)HistoryCoronavirus disease 2019 (COVID-19)SociologyEnvironmental ethicsLiteratureAestheticsGeographyArtSocial sciencePhilosophyComputer securityComputer scienceMedicine

Abstract

fetched live from OpenAlex

Focusing on Canadian poet Sue Goyette’s collection Solstice 2020. An Archive (2021), this article examines how dealing with the effects of a global pandemic through the medium of poetry can act as a powerful catalyst in raising awareness about collective vulnerability and mourning. During the locked-down days of 2020, Goyette felt it was her responsibility as a poet to find words to convey the sense of shared vulnerability people experienced in the face of a momentous event that confined them to their homes for days on end. Drawing on vulnerability theory, ecophilosopher David Abram’s thinking on the more-than-human world, Stacy Alaimo’s concept of trans - corporeality, as well as on recent theorizations on the COVID-19 pandemic, this article argues that Goyette’s Solstice 2020 is a most interesting sociological document that represents collective vulnerability, testifies to the conundrums posed by the still ongoing pandemic, and makes visible the deep affinities between humankind and the more-than-human world.

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)0.002
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.639
Threshold uncertainty score0.726

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0250.023
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.332
Teacher spread0.309 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueRevista de Estudios NorteamericanosSame topicGeographies of human-animal interactionsFrench-language works237,207