Porn scholars coming together: founders reflect on 10 years of the SCMS Adult Film History Scholarly Interest Group
Bibliographic record
Abstract
In this interview, participants recall the founding and history of the Adult Film History Scholarly Interest Group (AFH SIG) of the Society for Cinema and Media Studies (SCMS). Sharing their experiences as co-chairs, graduate representatives, and members, Dr Eric Schaefer, Dr Peter Alilunas, Dr Elena Gorfinkel, Dr John Paul Stadler, and Dr Feona Attwood discuss the circumstances of the early days of the SCMS AFH SIG. Their anecdotes illuminate the motivations and challenges faced by porn researchers at the groups’ inception and reflect on the SIG’s impact in years since. While documenting institutional origins through oral history, this conversation also considers how academic communities formed through the AFH SIG and similar channels have shaped developments in the field of porn research over the past decade.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".