Bibliographic record
Abstract
AR Cité is an augmented reality (AR) mobile app that accompanies users on an expedition through Montreal's Shaughnessy Village. Created by students, the gamified experience integrates the city's past into its contemporary landscape bringing to life historical depictions, personal stories, critical observations and artistic interpretations. The apps, with English and French versions, are available for iOS and Android devices and currently boast over 20 locations and 40 media vignettes, with more currently in production. The neighbourhood around Dawson College, where most of the AR experiences take place, is one of Canada’s most densely populated inner-city areas. It is steeped in history, controversial legacies and ongoing gentrification injustices. As students learn about the realities of their surroundings, they are inspired to bring creative reflections into the public sphere through augmented reality vignettes. Examples of the vignettes include an interview with Abanaki filmmaker Alanis Obomsawin, the Joe Rose story (a local LGBTQ activist who was murdered), the gentrification of the iconic Montreal Forum hockey arena, and dozens more. AR Cité guides users through pivotal milestones, gentrified structures, and transformative politics that have defined Montreal's identity. Beyond the colourful interface of this interactive game, AR Cité serves as a dynamic educational tool, igniting curiosity and fostering a deeper appreciation for our collective heritage through the innovative lens of augmented reality. What conclusions might be drawn about our communities, about how time changes narratives, obfuscates politics and the impact of historical events and how entangled actors can influence our collective memory. As a research-creation project, AR Cité continues to be a rich learning experience, bringing together diverse teams from a wide-range of disciplines. Producing works for the app has fostered a community of media producers as well as the now hundreds of intrepid users; teachers and students who are embarking on the AR Cité experience. But it has not all been smooth sailing, and the project team has learned much about the nature of producing location-based media as well as the impact of the app on its users. We continue to reflect on our experiences and adjust as production continues and a new cohort of students help to bring the project to the wider community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".