Deep classification: pornography, bibliographic access, and academic libraries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study examines the mainstreaming of pornography in the context of current economic, popular culture, and academic trends. As pornography becomes part of popular culture, it simultaneously becomes an area of focus for academics and therefore presents particular challenges for college and university libraries. Both physically and conceptually, academic libraries must find a place for pornography on the shelves and in the array of knowledge structured by bibliographic access systems. This study looks at how the variety of issues, concepts, and genres of pornography considered in academic discourse could be accommodated within access systems by examining the way in which the adult industry itself classifies pornographic films. Specifically, the terms used by the adult industry to classify these films could be grouped within newly developed categories. The identification of the categories would not be predicated on characteristics of porn films alone. Instead, the categories would encompass specific topics, concepts, and subject areas that connect pornography to mainstream culture. Using classifications from four different adult industry sources, four sample categories are presented that could serve as a model for how pornographic concepts could be accommodated within existing bibliographic access systems.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it