Animating Transit Tales: A Poetic Approach to Oral Histories, Discussing Gendered Narratives in Montreal's Métro Experience
Bibliographic record
Abstract
"Transit Tales" is a multi-disciplinary exploration documenting the gendered experiences of eight individuals navigating public spaces, particularly within the context of the Montreal Métro. Through oral history interviews, the study captures participants' voices, weaves together common themes such as self-censorship, spatial practices, and resistance against oppressive structures. Going beyond traditional documentation, the research incorporates visual and poetic elements to deepen narrative engagement and extend its reach to a broader audience. This research creatively navigates transcription techniques, and visual interventions to foster community interaction and stimulate conversation and self-reflection. By critically examining the intersection of oral history methodology and visual communication design, the research seeks creative inquiry for authentically capturing and responsibly sharing gendered experiences in an urban environment. It delves into questions of transcription processes, the representation of oral histories for critical engagement, and the graphic forms through which these narratives can be effectively communicated. Through this multi-faceted exploration, Transit Tales contributes to a deeper understanding of engendered and/or presenting as feminine people’s experiences in an urban transit environment while offering new perspectives on communicating oral history research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".