Bibliographic record
Abstract
Today, immersive technologies—like virtual reality—are celebrated as natural empathy machines, capable of fostering meaningful cross-cultural understanding. I interrogate this assumption through my case study of an early twentieth-century immersive, interactive ride: 20,000 Leagues Under the Sea (1903). The elaborate travel simulation and multisensorial, live-action scenes that followed offered millions of visitors a thrilling glimpse of the electrified future promised by American imperialism. Through 20,000 Leagues, audiences climbed aboard a simulated submarine and traveled to the Arctic (a massive refrigerated warehouse on Coney Island at the height of summer, featuring live polar bears and “authentic” Native Alaskans). Though perhaps experienced simply as entertainment, the ride was a potent pedagogical tool; the amusement introduced visitors to the thrill of “discovery” first-hand while erasing the violence of colonialism. The impact of this ride, and others like it, was profound, contributing to mass support for imperial wars abroad and racial segregation at home. Drawing lessons from my case study, I argue that the early ride was a precursor to twenty-first-century immersive worlds. My work centers on the pressing need to reconnect immersive technology to its historical context or risk reinscribing the imperial gaze into contemporary experiences. To move toward this goal, I offer fellow makers and scholars terminology to articulate the manifestations of the medium’s colonial inheritance, critical questions to guide a more equitable cultural production process, and a contemporary case study of VR film, Traveling While Black (2019), directed by Roger Ross Williams, who is already engaged in this critical work.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.018 | 0.046 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".