Narratives and Constructions of Sexuality in American Women’s Prisons
Bibliographic record
Abstract
This paper explores lesbianism in American prisons, identifying the impact dominant narratives have on incarcerated women with diverse sexualities. Drawing from academic research, the paper discusses historical theoretical perspectives and media construction of lesbian women in prison. Both theoretical and media portrayals of incarcerated lesbian women work together to alter public opinion. This paper seeks to explore how portrayals of lesbianism are harmful due to their tendency to stereotype and misrepresent. The paper will also explore how correctional perspectives continue to be affected by dominant narratives, which have a direct impact on the lives of incarcerated women through corrective measures. Finally, the perspectives of incarcerated women themselves are highlighted, as they are the experts of their own experiences. The term lesbian will be used throughout the paper as an umbrella term describing women who engage in sexual relationships with other women. This is for the sake of simplicity due to the limited scope of the paper’s length and the available research on the subject. It is not intended to dismiss or oversimplify the diversity of sexual identities and labels.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".