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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.998 | 0.999 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".