Climate Futures: A 360 mapping experience of Montreal under Climate Change
Bibliographic record
Abstract
The planet is facing massive climate change effects; Canada itself is warming at an unprecedented rate in comparison to the rest of the world. However as inland populations living in Montreal, we don’t often see these effects day-to-day. In addition, the media surrounding the future is often saturated with images of climate apocalypse that annihilate us. This makes it increasingly difficult to visualize a world that isn’t hopeless, and how we will survive in climate change. In this research-creation project, I question if there are hands-on methods to visualize a future world, where we thrive despite, and alongside, climate challenges. This research-creation project looks at the interacting aspects of climate change, critical mapping, speculative futurisms, and marginalized community voices through the creation of a ‘future map’ of Montreal. To explore this, I collect interviews from 6 community members/activists and render their visions into 360 images built into a digital map. I provide context with theories of world-building, mapping, and the creation process itself, bringing to life the participants’ visions of the future. I then describe how marginalized viewpoints and anti-oppressive frameworks build a better, tangible vision of tomorrow. I use the concept of remediation to explain the process of building futures in order to ‘fix’ society. To summarize, a critical mapping and 360 experience of Montreal under climate change will increase the body of work on climate change projection from a community perspective, and explore more embodied, hopeful, and grounded experience of climate futures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".