Bibliographic record
Abstract
I n August 2013, syndicated Canadian columnist, blogger, and radio talk show hostess Lori Welbourne took off her brassier and bared her breasts during an interview with Walter Gray, mayor of the city of Kelowna, British Columbia. Welbourne posted the video, with her exposed breasts pixilated, on YouTube. The “topless” interview, which was actually a staged stunt carried out with the connivance of the mayor, became a sensational global story. Within days, newspapers around the world picked up the story, and millions of people from all corners of the globe had watched the pseudotopless interview. Welbourne (2013), who champions what she calls a woman’s “constitutional right to bare her chest anywhere a man can bare his without cruel judgment,” had taken her camera operator to the interview in the mayor’s office and videotaped the stunt a few days before “Go Topless Day” to demonstrate that female toplessness was not illegal in Kelowna, British Columbia. Go Topless Day was a demonstration held on August 25, 2013, in Kelowna by a group of women. The attendees of the event turned out to be hordes of mostly male, camera-wielding voyeurs (Welbourne, 2013). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".