Unearthing History, Memory, and Creativity: A Park in Ottawa for Artistic Production
Bibliographic record
Abstract
The thesis seeks to explore the creation of a city park that reveals aspects of the site history and offer spaces to ignite public culture and artistic imaginations through discovery and making.It seeks to explore a relationship between the history of the site experienced through cuts -forms of excavation that bring up cultural memories -and how the language of material studies discovered through artistic processes can influence architecture in creating a space of memory and cultural activity for the community.Can the notion of a cut through the land be a way of experiencing the landscape and its history?Can the language discovered from techniques of making -screen printing and casting of materials and objects found on the site -be translated into material and texture and become architectural form?Can the relationship of the cut in the ground, the capturing of history, and the influence of material studies inform a place for discovering the forgotten histories of a site and serve as a model for overlooked or neglected places around Ottawa? Can the result -simply put, a public parkoffer ways to resist needless development and return public land to citizens?iii ii To Professor Inderbir Singh Riar.I learned so much!There were so many discoveries this thesis year.Thank you for showing me how to draw as extension of thought through the hand and to talk while I draw to activate the imagination.Thank you for the many, many hours you spent with me to push design thinking and thesis thinking further.The work is so much more because of it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.016 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".