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Record W4392470278 · doi:10.14201/candb.v13i143-157

“Hope, but also Danger”: A Conversation with Larissa Lai on not Going Back and the ‘Re’ of Recuperation

2024· article· en· W4392470278 on OpenAlexaboutno aff
María Jennifer Estévez Yanes, Sheila Hernández González

Bibliographic record

VenueCanada and Beyond A Journal of Canadian Literary and Cultural Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicUtopian, Dystopian, and Speculative Fiction
Canadian institutionsnot available
FundersMinisterio de Ciencia e Innovación
KeywordsConversationEnvironmental sciencePsychologyHydrology (agriculture)EngineeringCommunicationGeotechnical engineering

Abstract

fetched live from OpenAlex

Larissa Lai is a poet, fiction writer and academic who holds a Canada Research Chair at the University of Calgary, where she directs The Insurgent Architects’ House for Creative Writing. She has authored nine books. Her most recent works are The Tiger Flu, Iron Goddess of Mercy and The Lost Century. She is a recipient of the Jim Duggins Novelist’s Prize, the Lambda Literary Award, and the Otherwise Honor Book. She was recently awarded a Maria Zambrano Fellowship at the University of Huelva in Spain and has been actively engaged in cultural organizing, experimental poetry and speculative fiction communities since the 1980s. Her work often explores themes of identity intertwined with elements of science fiction and the fantastical imagination. This interview took place in Parque García Sanabria on 24th March 2023 during a visit of Larissa Lai to the University of La Laguna. This interview focuses on the convergence of history, myth and affects, providing a reflection on the circularity of time and the promise of happiness.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.969
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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 teacher head, 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

Citations0
Published2024
Admission routes1
Has abstractyes

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