Bibliographic record
Abstract
Rich connections between gaming and theater stretch back to the 16th and 17th centuries, when England's first commercial theaters appeared right next door to gaming houses and blood-sport arenas. In the first book-length exploration of gaming in the early modern period, Gina Bloom shows that theaters succeeded in London's new entertainment marketplace largely because watching a play and playing a game were similar experiences. Audiences did not just see a play; they were encouraged to play the play, and knowledge of gaming helped them become better theatergoers. Examining dramas written for these theaters alongside evidence of analog games popular then and today, Bloom argues for games as theatrical media and theater as an interactive gaming technology.Gaming the Stage also introduces a new archive for game studies: scenes of onstage gaming, which appear at climactic moments in dramatic literature. Bloom reveals plays to be systems of information for theater spectators: games of withholding, divulging, speculating, and wagering on knowledge. Her book breaks new ground through examinations of plays such as The Tempest, Arden of Faversham, A Woman Killed with Kindness, and A Game at Chess; the histories of familiar games such as cards, backgammon, and chess; less familiar ones, like Game of the Goose; and even a mixed-reality theater videogame.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.017 |
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".