Narrative Debris: Counter-Mapping Overlooked Socio-Political \nStories of Montreal’s Quartier des Spectacles
Bibliographic record
Abstract
Narrative Debris: Counter-Mapping Overlooked Socio-Political Stories of Montreal’s Quartier des Spectacles \n \nPatricia Enns, M.Des. \nConcordia University, 2023 \n \nThis thesis-creation bears witness to overlooked social and political narratives of Montreal’s Quartier des Spectacles, a historically and culturally rich area undergoing rapid gentrification and commercialization since 2003 (Lam, 2007). \n \nThe creation project, Narrative Debris (2021) consists of a public facing website and participatory kit. The website presents an illustrated map of the Quartier des Spectacles which invites visitors to explore experiential feedback from participants of an audio walk. The participatory kit offers the public an accessible tool to create their own paper-making debris-mapping of the neighbourhood. The goal of the research is to challenge the area’s current monolithic narrative as a place of commercialized entertainment by using embodied, materially engaged, and participatory methods. The research emerges from a series of iterative walks leading to new sensory, temporal and qualitative approaches to mapping. These techniques captured the subjective, material, and gestural dimensions of Quartier des Spectacles, and examined how maps could amplify alternative socio-political narratives. \n \nDebris-maps: hand-made paper sheets created using debris collected from the Quartier des Spectacles, were developed as a counter mapping strategy. Examining what discarded remnants can tell us, the process highlighted local social phenomena such as the opioid crisis and the impacts of the recent acceleration of gentrification. This process exposed the importance of inviting others into the research. Informal interviews, an audio walk, and participatory kits were used to engage with local participants’ experiences and histories of the area.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".