Bibliographic record
Abstract
This dissertation addresses the social significance of parlour games as forms of cultural expression in medieval and early modern England and France by exploring how the convergence of textual materialities, players, and narratives manifested in interactive texts, board games, and playing cards. Medieval games, I argue, do not always fit neatly into traditional or modern theoretical game models, and modern blanket definitions of ‘game’—often stemming from the study of digital games—provide an anachronistic understanding of how medieval people imagined their games and game-worlds. Chapter 1 explores what the idea of ‘game’ meant for medieval authors, readers, and players in what I call ‘game-texts’—literary texts that blurred the modern boundaries between what we would consider ‘game’ and ‘literature’ and whose mechanics are often thought to be outside the definition of ‘game.’ Chapter 2 examines how recreational mathematics puzzles and chess problems penned in manuscript collections operate as sites of pleasure, edification, and meditative playspaces in different social contexts from the gentry households to clerical cloisters. The mechanics, layout, narrative, and compilation of chess problems rendered them useful for learning the art and skill of the game in England. Chapter 3 traces the circulation, manuscript contexts, and afterlives of two game-text genres in England—the demandes d’amour and the fortune-telling string games—in order to understand how they functioned as places of engagement and entertainment for poets, scribes, and players. Chapter 4 illustrates how narrative and geography became driving forces for the development and rise of the modern thematic game in Early Modern Europe. This chapter charts how changing ideas of spatiality enabled tabletop games to shift from abstract structures enjoyed by players in the Middle Ages, in which game narratives take place off a board, to ludic objects that incorporated real-life elements in their design of fictional worlds—thereby fashioning spaces that could visually accommodate narrative on the board itself. This dissertation places games into a more nuanced historical and cultural context, showing not only the varied methods by which medieval players enjoyed games but also how these ideas developed and changed over time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".