It Takes a City : Installation for Place-Based Learning in Montreal
Bibliographic record
Abstract
While environmental education programs have historically been conducted in both urban and rural contexts, today the stimulation and educational resources that can be found in urban settings outside school buildings are often overlooked. A city like Montreal offers a wealth of objects, peers, skill opportunities and informal educators, but they mostly remain isolated from schoolchildren by distance, the enclave nature of institutional schools, and cultural norms that remove autonomy from children as subjects unfit to traverse the “dangerous city” on their own. The project puts forth the neighborhood of Rosemont as the locus for an “exploding school” of the Streetwork framework, modelled by Colin Ward and Anthony Fyson. The proposed installation, making use of a recently vacated building in the underdeveloped East end of the busy Masson Street, is one shoot of an educational rhizome–a place chosen not to be a container for education, but conceived as a knot where a multiplicity of learning paths are bound together. Working as an access point to existing community networks like the citizen-organized ruelles vertes, and situated near the existing overflowing public schools, the project offers itself as a waystation for those on different educational journeys in, along, and beyond the city itself. They may learn from studying their environment that any built fabric is malleable, and not an immutable reality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".