Bibliographic record
Abstract
Abstract Inspired by, among other things, a coffee pot and a fishbowl, on April 14, 1996, Jennifer Ringley launched Jennicam. In a self-described “social experiment,” whose diverse objectives included connecting with family and friends and challenging mainstream media images of women with “perfect hair and perfect friends,” regularly refreshed still images of Ringley’s home were uploaded to Thanks to Lisa Feinberg, Bridget McIlveen, and Sharon Marcushamer for their wonderful research and editorial assistance on this chapter, as well as to the students whose opinions and ideas have been inspirational in this and other projects, including Julie Shugarman, Louisa Garib, Brad Jenkins, Rafael Texidor Torres, and José Laguarta Ramirez. Thanks also to an anonymous peer reviewer and to my colleagues on the ID Trail project—George Tomlinson, Mary O’Donoghue, Shoshana Magnet, Ian Kerr, and Michael Froomkin for their extremely insightful commentary and suggestions on earlier versions of this chapter. All errors, of course, remain my own.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".