Bibliographic record
Abstract
On May 14, 2002, a curious scene was enacted on the stage of the Shaw Festival Theatre in Niagara-on-the-Lake. In a New York City precinct police station, the setting for Neil Munro’s acclaimed production of Sidney Kingsley’s Detective Story, a mysterious trench-coated figure in the obligatory slouch-brimmed fedora entered into the half-light. As the figure roamed among the desks, a second figure, similarly clad, entered behind the first: A gun blazed; the first fell victim to the second, the victim’s fedora rolling away across the floor. Calmly, the newcomer stood over the body, then deliberately picked up the fallen hat. Looking it over, the murderer came to a decision. The fallen hat replaced the fedora on the head of the stranger, the rejected hat was tossed onto the floor beside the body. The murderer posed proudly in the dim room. As the assembled members of the Shaw acting company laughed and cheered, the lights came up and the figures took their bows: Christopher Newton, outgoing Artistic Director, swiftly recovering from the murderous shot, and Jackie Maxwell, Artistic Director Designate, stepping dramatically into the Shaw spotlight.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.035 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".