To read what was never written: the licentiousness of history in Alan Moore and Melinda Gebbie’s<i>Lost Girls</i>
Bibliographic record
Abstract
Lost Girls by Alan Moore and Melinda Gebbie (2006. Marietta, GA: Top Shelf Productions) incorporates a variety of both historical and fictional sources into its sexually explicit narrative of three women recounting their troubled pasts. Many of these sources are represented through adaptation and pastiche. This article examines Moore and Gebbie’s strategy of mimicking rather than merely alluding to the intertexts that shape the narrative of Lost Girls. Further, since many of their sources are showcased through the metafictional technique of mise en abyme, I argue that the mimicry of the comic draws attention to the dangers involved in processes of interpretation. The reader of Lost Girls is self-reflexively interpellated by the comic as a reader of history, adaptation and pornography, and as such is prompted to address a series of related distinctions that often shape processes of value-assessment: between the factual and the fictional, the authentic and the forged, and the literary and the obscene.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".