Bibliographic record
Abstract
Andy Warhol famously quipped that in the future everyone would have 15 minutes of fame. He never mentioned that to obtain your moment in the spotlight you might have to acquire Brad Pitt's cheekbones or Pamela Anderson's breasts. I Want a Famous Face , a new MTV series, features seven individuals hoping to acquire the famous face of a pop star (www.mtv.com/onair/i\_want\_a\_famous\_face). Two participants, Mike and Matt Schlepp, conclude that their lives would improve if they acquired Brad Pitt's looks. To resemble Pitt, the brothers undergo rhinoplasty,receive chin implants, and obtain porcelain veneers. Sha is an aspiring model; she wants Pamela Anderson's physical features. She receives breast and lip implants and undergoes liposuction. Mia works as a Britney Spears impersonator; she wants Spears' “perky breasts.” Jessica, a transsexual formerly known as Michael, receives breast implants, cheek implants, and an eyebrow lift. She wants to resemble Jennifer Lopez. Jennette submits to a body lift after already undergoing gastric bypass surgery. She wants Kate Winslet's “full-figure” look. …
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.011 |
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".