Landscape as Method and Model: Developing Research-Creation in \nCommunity Through Landscape Painting and Pedagogy in Montréal’s Sud Ouest
Bibliographic record
Abstract
This dissertation takes up landscape theory, community-engaged art education, and research-creation methodologies to ask how the creation of artworks can reveal insights into the social, political, and economic foundations of place, and how community artmaking can be used as inductive research to document experienced changes in place over time. This thesis conducts two concurrent projects that focused on Montréal’s Sud Ouest borough, the site of substantial re-development over the past 50 years. The first is a personal research-creation project using plein air landscape painting to theorize painting as fieldwork and research creation. The second was an eight-week community art class called Landscaping the City, which used community-based research creation methodology to conjoin participant artmaking with longform interviews. 17 participant-students engaged in a curriculum focused on the neighborhood’s past, present, and future, balancing skill building with conceptual concerns. To carry out these projects, I embedded in a small community art school and a grassroots anarchist development project which gave insight to how community members have self-organized to meet citizen needs. By thinking these projects together, this thesis theorizes how multimodal engagement with the built environment can help to democratize forms of engagement, make visible contradictory demands and desires for space, and foster civic interest and participation in processes of placemaking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".