The architecture of Ontario Place: reinvigorating the commons through adaptive-reuse and operative landscapes
Bibliographic record
Abstract
Ontario Place, located in Lake Ontario along Toronto’s shoreline, was always \nmeant to be for the public. \n In its current conditions, the ongoing neglect by the \nGovernment of Ontario for the last several years, has led to the degradation and \ndisrepair of many of the structures and landscape of the site. How can Ontario \nPlace be reimagined as a commons for the city of Toronto and the province \nof Ontario to experience and celebrate the waterfront? The islands of Ontario \nPlace are reimagined through the lens of nested scales of intervention ranging \nfrom the Great Lakes watershed, to the city of Toronto, the waterfront, and \nthe five Pod buildings on the site. Historic-interpretive research was completed \non the designers of Ontario Place, megastructure precedents, and site studies \nof the current conditions. The knowledge gained from the research and site \nanalysis of Ontario Place influenced a series of architectural and environmental \ninterventions to the site. \nThe design interventions take into consideration both the landscape and \narchitectural re-mediation and re-imagination of a new commons using \nsustainability, ecology, rewilding, and interactive play/ learning as key \ncomponents of the design for a new operative landscape. A living breakwater off \nthe shores of the islands, a data collection archipelago around the Great Lakes, \nwetland planting, water filtration and ruin demolition for replanting remediate \nthe landscape of Ontario Place. An adaptive-reuse of the out-of-commission Pod \nmegastructures, strips the current skin of the buildings to expose the structural \nframe underneath. This frame is loaded with plug n’ play containers that hold \nvarious public programs. These containers are plugged in and out seasonally, \nrefreshing and molding to the needs of the community. Greater impacts of the \nproject aim to generate more public green space along the Toronto waterfront \nfor the community in the midst of COVID-19, create a pilot project for the health \nof the Great Lakes system and education of the public, as well as continuing \nthe recent reclamation of the waterfront from industry to public space by \nWaterfront Toronto for all people to enjoy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".