The Politics of Algorithmic Hyper(in)visibility: BIPOC, LGBTQ+ and Erotic Romance Fiction on Amazon
Bibliographic record
Abstract
Romance fiction has a complicated relationship with visibility. It is a genre with undeniable presence in the contemporary publishing industry in terms of output and sales, but has historically been hidden in personal, rhetorical, and material ways. Within the genre, BIPOC, LGBTQ+, and erotic romance writers and their works experience a kind of hyper(in)visibility, a concept which describes how non‑normative identities and bodies are overlooked, ignored, and underrepresented, while simultaneously subject to intense scrutiny, surveillance, and stereotyping. Semi‑automated decision‑making technologies, including content moderation and search and recommendation algorithms, further compound these invisibilizing forces. Drawing on interviews with authors, metadata analysis, and critical discourse analysis of terms of service, this article argues that algorithmic systems maintain a politics of hyper(in)visibility for BIPOC, LGBTQ+, and erotic romance authors and books, which risks exacerbation in light of the broader deplatfomization of sex online and current conservative political movements, particularly in the United States.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".