Bibliographic record
Abstract
“And where does the dog come in?,” the players in Shakespeare in Love keep asking. This is one of the film's running gags about setting supposed high art within the context of hard economic forces. Dogs pull the punters in, it is implied; so to be popular a play must have its dog and, by wider implication, its clown scenes. The joke is based on some truth. Several extant plays have scenes with dogs, and many more have their clowns. Some even incorporate bears. Mucedorus (1588-98), one of the most popular plays on the public stage, reprinted at least fifteen times over seventy years, has a scene early on in which Mouse, the clown, probably first played by the great Richard Tarlton, exits backwards tumbling over a bear, and Shakespeare's The Winter's Tale (1610) surely glances at this tradition in the famous stage direction that has Antigonus “ Exit pursued by a bear ” (3.3.58). Bears would not be difficult to get hold of, particularly as some playhouses did double duty as bear pits, but a bear part might sometimes be taken by a man in a bear suit. Mouse jokes about the probability that the bear rumored to be on the loose “cannot be a Beare, but some Divell in a Beares Doublet” (1.2.3-4). The joke could work either way, allowing for either a real bear or a man in a bear suit to collide with Mouse.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.246 | 0.086 |
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".