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Record W3011149911 · doi:10.1177/1532708620912802

Pulping as Poetic Inquiry: On Upcycling “Upset” to Reckon Anew With Rape Culture, Rejection, and (Re)Turning to Trauma Texts

2020· article· en· W3011149911 on OpenAlexafffund
Amber Moore

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsPoetryMeaning (existential)SociologyAestheticsLiteratureArtPhilosophyEpistemology

Abstract

fetched live from OpenAlex

This article explores an experience of “pulping,” a rejected poetry inquiry; that is, the author describes revisiting and rewriting a micro poetry cluster about rape culture and teaching trauma texts nixed by reviewers for being too “upsetting.” This project aims to (a) demonstrate the potential of poetic inquiry for “pulping” refused art, (b) resist silencing of sexual violence, and to (c) call for creative “upcycling” of upset. The author returns to her rejected poems and engages in a new poetic inquiry which she conceptualizes as a kind of feminist “pulping” process where she “upcycles” her troubling writing in search of newfound fecundity. As such, by reworking the refusal, reckoning with unpublished refuse, and staying with the trouble in re/fusing new art, she engages in poetic inquiry as a pulping process to (re)make meaning from an experience of academic silencing of art that addresses sexual assault and rape culture.

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)0.010
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0170.078
Scholarly communication0.0170.012
Open science0.0020.011
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0050.001

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 head0.255
GPT teacher head0.421
Teacher spread0.165 · 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 designQualitative
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

Citations8
Published2020
Admission routes2
Has abstractyes

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