COVID-19 as a super crisis: implications for place management
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate COVID-19 as a super crisis in the design and management of places. Design/methodology/approach This theory-driven work outlines why and how, by treating COVID-19 as a super crisis, the immunological view rises in priority and swiftly ushers in short- and long-term implications for space design and place management. Findings First, this paper looks at the short-term impact of COVID-19 upon space and place management in addressing how porous bubbling, stippling and flexible curtaining respond to immediate retrofitting needs during the pandemic. Using the concept of COVID-19-induced collective trauma, this paper draws attention to health-care facilities, schools, workplaces, commercial buildings and public outdoor spaces. These sites require short-term improvisation in place and space design and will, where the collective trauma of COVID-19 leaves strong traces, require long-term redesign and rethinking. Social implications As a super crisis, COVID-19 generates contradictions in the existing trend in space and place studies from the notion of space and place as a container to one focusing on “flow.” A focus on flow highlights a focus on space and place as adaptable to changes in flow, especially as augmented and mediated by technology. Originality/value This treatment of COVID-19 as a super crisis is intended to stimulate the design and management of spaces and places in the post-COVID-19 period.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.050 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".