Pulping as Poetic Inquiry: On Upcycling “Upset” to Reckon Anew With Rape Culture, Rejection, and (Re)Turning to Trauma Texts
Bibliographic record
Abstract
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 As such, by reworking the refusal, reckoning with unpublished refuse, and staying with the trouble in re/fusing new art, she engages in poetic inquiry as a pulping process to (re)make meaning from an experience of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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".