Mapping Montreal Flaneurs' Stories
Bibliographic record
Abstract
Although traditionally, the flaneur has been attached to the specific geo-historical context of \nnineteenth-century Paris, flaneurs have evolved throughout the twentieth century and continue to \nmeander in contemporary cities. Flaneurs not only meander; they also tell stories about their \nmeanderings, including stories about a city, a neighbourhood, a street, a park, a bench and about the \ndifferent people and non-human creatures they encounter. Given the detail-oriented trait of the \nflaneur's personality and their capacity to act as a connoisseur and a collector of unnoticed, invisible \nand neglected city's components, these stories provide a unique perspective on the city, on its \ninhabitants and their behaviours. In this project, I investigate flânerie as a critical tool to reflect upon \nthe relationships between people, objects, and places through the mapping of these stories. My journey \nin the world of flanerie starts with the identification of contemporary flaneurs who have been telling \nstories about their flaneries, with a particular focus on Montreal. Among the rich material these \nflaneurs collected and shared, I selected seven stories from a printed magazine (i.e. the Flaneur \nMagazine) dedicated to flaneries along rue Bernard from Outremont to Mile End. To map these \nstories, I developed a graphic language dedicated to representing various spatio-temporal and personal \naspects of these stories. This language was inspired by the concept of Inductive Visualization \n(Knowles et al. 2015), which allows for the spatial expression of a story based on its content, in \ncontrast with conventional euclidean cartographic structure. This approach led me to produce The \nFlanerie Atlas of Rue Bernard, which corresponds to the creation part of this research-creation project. \nThroughout the production of this original atlas, I was able to develop a methodology to map data \nfrom stories and to propose a new (carto)graphic language dedicated to the representation of stories. \nThis atlas revealed certain particularities of Rue Bernard as captured by flaneurs' stories. It led me to \nreflect on the relevance of mobilizing flaneurs' materials to study a park, a street, a neighborhood. \nFlaneurs' materials, in this sense, show their potent to reveal people's affective bonds with places.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".