Bibliographic record
Abstract
This article presents audio found poetry as an approach which positions participants’ voices in the heart of the inquiry. The methodology was influenced by radio autoethnography and audio papers where theory, voices, and sound are combined to create a new aural experience—an approach that argues that it is essential that the audience listens rather than reads. These two audio found poems share the voices of 14 participants from Australia, United Kingdom, North America, and Mainland Europe, interwoven with the author, talking about their relationship with underwear. Participants recorded their own story. Each voice was edited using Audacity (a software program) and then different voices were joined together. Two poems emerged. Audio 1: Practical Underwear shares stories from day-to-day underwear preferences and stories of those who do not wear underwear. The stories in Audio 2: Dress Code Red are connected to sexuality and political aspects of underwear. Framing the work through the lens of new materialism creates a space of agency for an entanglement between the underwear and the human voices speaking about it, which in turn affects the embodied experience of the listener. What stories could your underwear tell?
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".