<i>Porno Cultures Podcast</i>: graduate student roundtable
Bibliographic record
Abstract
The Porno Cultures Podcast was created in 2017 by Brandon Arroyo while finishing his PhD in Film & Moving Image studies at Concordia University. After being a fan of podcasts for years, Brandon became frustrated that there were hardly any shows featuring the authors or ideas that he became familiar with while writing a dissertation centred on pornography studies. So, he decided to start his own show featuring academics writing about pornography. The podcast also includes performers, directors, bloggers, and playwrights who are helping us think about pornography within our culture in new and interesting ways. This is the podcast where we think about pornography rather than react to it. This is a partial transcript from an episode featuring four up-and-coming graduate students and recently minted PhDs talking about their experience working on pornography studies while in graduate school. The guests include John Paul Stadler, PhD in Literature with a certificate in feminist studies from Duke University, Darshana Mini, a PhD candidate in Cinema and Madia studies at the University of Southern California, Ben Strassfeld, PhD in Screen Arts & Cultures from the University of Michigan, and Madita Oeming, a PhD candidate in American Studies from the University of Paderbon in Germany. This conversation took place at the 2018 edition of the Society for Cinema and Media Studies Conference in Toronto, Canada. To listen to more episodes and find out more about the podcast, check out the website (pornoculture.podomatic.com).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.215 | 0.082 |
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".