Bibliographic record
Abstract
In this study, I provide applied examples of using cut-up poetic inquiry as an arts-based research method for analyzing erasure poetry. The erasure poetry was composed by five poet-participants and me during a sensory ethnography that explored embodied experiences of a sexual educator training program. I first overview erasure poetics in the context of sexuality education. I explain how erasure poetry as method can interrupt authoritative proclamations of truth, while also providing a technique to grapple with complex, corporeal data – central topics in sex education research. I then theorize cut-up poetic inquiry as an additional form of erasure, asking and illustrating how the processes of cut-up can distill information to enable emergent analytic insights in the context of my research. Throughout, I meditate on how erasure poetry as an arts- based research method can contribute to discussions of language, discourse, and embodiment in sex education research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.064 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".