Bibliographic record
Abstract
In a time of intensifying xenophobia and anti-immigration measures, this book examines the impulse to acquire a deeper understanding of cultural others. Immersions in Cultural Difference takes readers into the heart of immersive simulations, including a simulated terrorist training camp in Utah; mock Afghan villages at military bases in Canada and the UK; a fictional Mexico-US border run in Hidalgo, Mexico; and an immersive tour for settlers at a First Nations reserve in Manitoba, Canada. Natalie Alvarez positions the phenomenon of immersive simulations within intersecting cultural formations: a neoliberal capitalist interest in the so-called "experience economy" that operates alongside histories of colonization and a heightened state of xenophobia produced by War on Terror discourse. The author queries the ethical stakes of these encounters, including her own in relation to the field research she undertakes. As the book moves from site to site, the reader discovers how these immersions function as intercultural rehearsal theaters that serve a diverse set of strategies and pedagogical purposes: they become a "force multiplier" within military strategy, a transgressive form of dark tourism, an activist strategy, and a global, profit-generating practice for a neoliberal capitalist marketplace.
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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 | 0.005 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".