Transients, punks and hobos: rethinking the history of train hopping through experimental film
Bibliographic record
Abstract
While in social history and mobilities research, the study of railway mobility is becoming increasingly popular, offering new insights into (trans)national railway cultures, there is less interest in illicit mobility, unconventional modes of travel and non-regulated, irregular patterns of movement, such as train hopping, and the way they function in experimental film. To fill this gap, I build on the recent mobilities literature to discuss two stylistically distinct experimental films, Reading Canada Backwards (Steve Topping, 1995) and Portland (Greta Snider, 1996), which offer a social commentary on train hopping, typically associated with the history and material conditions of North American railway travels. Challenging the larger freighthopping mobilities discourse, both films confront the historical legacy of the hobo, as reimagined by occasional transients and punk drifters, as a product of capitalist enterprise, railroad transportation services and failed bourgeois masculinity. While in narrative and documentary films, hobo culture often emerges as an alternative, intrepid lifestyle and a personal philosophy based on economic or environmental concerns, Reading Canada Backwards and Portland take a more critical and ironic take on riding the rails, highlighting its casual spontaneity, playful creativity and affective potential, which questions the drifter as an active agent of marginal mobility practices.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".