Disorienting Americans: Infrastructures of Mobility in American Fiction and Film, 1890-1930
Bibliographic record
Abstract
Disorienting Americans re-evaluates the meaning of disorientation in American fiction and film from 1890 to 1930. Focusing on texts that represent infrastructures of mobility—the hotel, the railroad, and the urban street—this dissertation identifies what I call the disorientation episode as an important narrative form that constructs the larger cultural significance of mobility while working to recalibrate modes of realism. Building on scholarly analysis of the roles of infrastructure and popular entertainment in shaping modern subjectivity, I challenge a prevailing account of how realist form works to secure the integrity of the individual by containing the disorienting effects of urban modernity and consumer culture. Close readings of infrastructural settings demonstrate how fiction and film cultivate rather than dissipate the tensions between a series of interpretive frameworks for mobility, adapting realist modes to new arrangements of private and public space. On this basis, I argue that the disorientation episode represents a realist effort to recuperate the self-possessed individual by constructing it in direct contact with infrastructures that generate these tensions.Chapter One focuses on disorientation episodes in the hotel settings of Stephen Crane’s “The Blue Hotel” (1898) and Willa Cather’s “Paul’s Case” (1905), in which disorientation reveals the instability of a national fantasy of mobility and progress that attempts to provide an alternative normative structure for masculine gender roles in the absence of domestic ideology. Turning to narratives depicting women in railroad settings, Chapter Two analyzes Theodore Dreiser’s Sister Carrie (1900) alongside “white slave” fiction (a genre describing the supposedly widespread “traffic in women”), early train films, and railroad advertisement images. Here, the disorientation episode interrogates the cultural response to the passenger car, complicating the effort to contain anxieties about the disorienting effects of mobility by constructing the female passenger as uniquely vulnerable. Finally, Chapter Three establishes how realism uses the urban street as a vehicle for mediating the individual’s relationship to crowds and to categories of race, with close readings of carnivalesque performances in the streets of Henry James’s The American Scene (1907), King Vidor’s film The Crowd (1928), and Charles Chesnutt’s The Marrow of Tradition (1901).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".