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
In preparing my talk for a panel on “Whiteness and Technoculture” for the Society for the Social Study of Science in Boston, I wanted to think about the relationship of my research on the technocultures of the online pornography industry to the events in Charlottesville, which occurred only weeks earlier. Two trends within the online pornography industry came immediately to mind. The first is the aesthetic of “white innocence” as sexual fantasy that reveals a cultural conversation between the mainstream gay pornography industry and white nationalism in the United States. The second is the emergence of affiliate networks that aim to curate content for “unique male viewers” because the internet is, curiously, awash in “female-focused” content. Both of these phenomena seem particularly relevant at a time when white fragility, toxic masculinity, “men’s rights,” and xenophobia have been given explicit approval by the newly elected U.S. President, Donald Trump. These forces have long defined the United States, but they also reveal the way in which this presidency is uniquely awful and dangerous.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it