Board(er) Games: Space, Culture, and Empire in <i>Jumanji</i> and Its Intertexts
Bibliographic record
Abstract
Two recent transmedia narratives—Karuna Riazi’s 2017 middle-grade novel The Gauntlet and the 2017 film Jumanji: Welcome to the Jungle—have attempted to reclaim the 1995 film Jumanji’s colonial narrative (adapted from Chris Van Allsburg’s 1981 picture book). Both present forms of the “portal fantasy,” in which a protagonist supernaturally breaches the borders of another world. The Gauntlet transports its Muslim Bangladeshi American protagonist to a fantastical board game, whereas Jumanji: Welcome to the Jungle reconfigures the genre as multimedia immersive gameplay in a fictional “other” realm. Although these reworkings seemingly destabilize white supremacy by centring multiethnic American identities, their negotiations with the board game, itself a product of imperial history and a manifestation of the “gamification” of empire (wherein progress is measured by control of the board) complicate this. The creation of an American neo-colonial nationalism through a system of orientalizing these fantastic spaces (the jungle within the 2017 film and Riazi’s clockwork Islamic city) affirms the need for their control or eventual destruction by the protagonists. This effectively creates cultural borders that extend into these fictional spaces, playing out historical systems of empire in a bid to gain access to neo-empire.
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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 | 0.002 |
| 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 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".