Un-making architecture: an introduction to a critical framework
Bibliographic record
Abstract
This special issue of The Journal of Architecture surveys the concept of 'un-making' as an overarching facet of architectural thinking and production that has yet to be considered at a synoptic scale.When we refer to 'un-making', we mean the actions that result in the dismantling of architectural forms, modes of thought, and means of production.A historical study of these operations, we hope, might generate necessary theoretical frameworks to conceptualise transformations in architecture amid today's unprecedented socio-political and environmental challenges.The aim of this special issue is twofold: first, it brings into dialogue topics from across different periods and geographies that explore varied yet related notions of un-making; second, it introduces a range of theoretical approaches to analyse architectural disassembly that might further conversations and actions to reimagine the discourses, institutions, and practices in the field today.How might past and ongoing instances of architectural destruction paradoxically help us develop more critical and comprehensive methods to undertake the study, reform, and design of the built environment?Through the lens of this issue's collected historical studies, we argue that mechanisms of architectural un-making in design, environments and technologies, and politics are interdependent and must be considered in tandem to address the extraordinary architectural challenges of the twenty-first century.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".