Private in public: reimagining spatial design in a physically distanced world
Bibliographic record
Abstract
In March 2020, the novel coronavirus disease (COVID-19) was officially declared a global pandemic. Within a span of 2 years, the pandemic resulted in over 6 million documented deaths and triggered large-scale economic crises as well as social upheaval. Reactive measures such as national lockdowns, isolation and quarantine procedures, and physical distancing practices were instituted for the benefit of public health and safety. However, minimal consideration was given to the psychological impact this break in human connection would cause. As COVID-19 has receded to an endemic and public spaces have reopened, the central concern of this practicum is the redesign of performance interiors to observe suggested public health guidelines and establish a standard for safe socialization in a post-COVID world. This will take the form of a post-pandemic theatre centre – a redesign of the Royal Manitoba Theatre Centre in the Exchange District of Winnipeg. In this practicum, I explore lessons from past and present pandemics, the evolutional journey of performing arts typologies, and theories of Proxemics and Effective Capacity to inform the user capacity, spatial organization, furniture selection, and ventilation and sanitation systems of the theatre centre.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.030 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".