Architecture after CovidInteriors in the Era of Covid-19: Interior Design between the Public and Private Realms
Bibliographic record
Abstract
Will our homes ever be the same? What did architects learn from the pandemic? Two new books from Bloomsbury Publishing explore the wide-ranging impact of Covid-19 on houses, cities, and designers. Despite coming from the same publisher, the books are radically different. Architecture after Covid by architectural educator Albena Yaneva is a slim, 156-page monograph, in a single voice, articulating a coherent argument about architecture during and after the pandemic. Interiors in the Era of Covid-19 presents multiple voices and perspectives, as a collection of twenty essays, edited by five editors from Kingston University: Penny Sparke, Ersi Ionnidou, Pat Kirkham, Stephen Knott, and Jana Scholze. Even individual chapters present multiple voices, as no less than nine are coauthored. Both books communicate an urgent desire to record changes in the built environment after March 2020, when most of us worked suddenly from home and watched in horror as SARS-CoV-2 spread around the world, taking nearly 7 million lives. Both books, too, present some surprising, almost counter-intuitive findings.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".