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Record W4313412657 · doi:10.4000/erea.15538

Persephone

2022· article· en· W4313412657 on OpenAlexaff
Laure-Hélène Anthony-Gerroldt

Bibliographic record

VenueE-rea · 2022
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)
Fundersnot available
KeywordsPoetryStanzaLiteratureArtPsycheMythologyVisual artsPaintingPerspective (graphical)RhymePsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

This piece of creative writing is a single piece from a larger collection of poems that explores different sides of love, with a special focus on longing and waiting, but also tackling darker aspects, such as abuse and rape. Using a mythical background and such figures as Persephone, Eurydice, Orpheus, Psyche and Artemis, the collection aims to convey softness and calm, while also representing empowered feminine voices. The poem is composed of varying stanza forms and rhythms mixed with prose and using motifs and colours that also occur in other poems of the collection to create echoes within the piece. It is also part of a wider artistic project that includes paintings, photographs, a short film and other creations, which all work as so many reverberations and refractions of the poems themselves.The project was partly inspired by H.D.’s poem “Eurydice,” a piece that really made me want to work with myth and to give a voice to women whose perspective is rarely portrayed in literature. At the time, I was also working on my doctoral thesis, especially on contact and touch, and on representations of love in poetry. As I kept working on poetic voice and poetic representations of sensations and breathing, I realised that through writing more poetry, I could also get a different hold of the creative process that still nourishes my thoughts on the relationships between poetry, sensation, empathy and healing.

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: Empirical · Consensus signal: none
Teacher disagreement score0.446
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 studies0.0020.001
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.4460.192

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 head0.317
Teacher spread0.294 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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