Bibliographic record
Abstract
Who holds the right to decide what gets remembered? Conversely, the right to forget? The interior architecture installation, Memory in Suspension, exhibited in Toronto’s Chinatown West as part of Museum of Toronto’s 2020 Intersections Festival at Cecil Community Centre, combines new and old technologies to tell the forgotten stories, wilful omissions, and accumulation of silences that exist beyond Toronto’s official heritage definition of its Chinatown neighbourhoods. Foregrounding the lack of records and archival materials available, Memory in Suspension develops an alternative approach to heritage reconstruction when confronted with a historically significant interior which has no architectural records or documentation. By unearthing the unrecorded histories of the first Chinese owned business in Toronto, Sam Ching & Co. Chinese Laundry, we explore what marginalised communities have known for some time—namely, all that is recorded is not necessarily all that is, and what is remembered extends far beyond what is recorded. Through interior architecture, Chinatown Lost and Found asks what we choose to remember and which tools and technologies keep those memories alive. This article explores how interior architecture can create a dialogue between official history and the associative nature of lived experience. Learning from these productive tensions, we suggest how interior architecture can use old and new archival technologies to empower community stakeholders to safeguard the future heritage(s) of Toronto’s Chinatowns. In particular, this article links 3D scanning technologies to community memory and marginalisation to pursue a dynamic and reversal-based interior architecture approach that critically positions how subjects inhabit, constitute and are constituted by the spaces in which they find themselves. In doing so, this article offers a more holistic approach and account of the instability of space and time in relation to memory and heritage for interior architectural practice.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".