Bibliographic record
Abstract
This article argues that to understand the role and place of the foreskin, we must address the aesthetic question that sits at its root. North American media often describe the foreskin as “ugly,” “gross,” or pejoratively “European”; all of which present, fundamentally, an aesthetic comment on what is pleasing. As such, this article investigates the aesthetic discourse surrounding the foreskin in relation to a range of materials that speak at or around the foreskin. In particular, it looks at sources deemed to be “common”—sex manuals, pregnancy manuals, and film and television—alongside theoretical and scientific studies. Undertaking a close reading of these materials, this article sheds light on the striking similarities that these distinct bodies of literature share and the way that aesthetics undergirds their arguments, often as a silent statement rather than exerted forcefully. Through this argument, this article breaks new ground on the way that we consider the foreskin, and, importantly, the aestheticization processes that shape our understanding of this seemingly ancillary component of the penis. Accordingly, this article contributes to a growing body of scholarship on the politics of the foreskin and circumcision by shifting the debate to consider the aesthetic.
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 | 0.005 | 0.008 |
| 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 | 0.067 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".