"Hostile architecture" and its confederates: A conceptual framework for how we should perceive our cities and the objects in them
Bibliographic record
Abstract
In recent years “hostile architecture” or designs (also called “disciplinary architecture” and “defensive architecture”) has become an ever more common feature of our cities. Examples of these designs are benches you cannot sleep on, spikes you cannot stand on, and metal plugs you cannot skate on. These designs have created an outrage among activists and the general population since they have largely been conceived as an attack on the worst-off and there is an increasing academic body of work mostly looking into their design features, the motivations behind them, but also whether and under what condition they should be used. Although progress has been made on the issue of these forms of architecture/designs, no clear definitions currently exist for “hostile architecture,” (etc.) and their related concepts, which are especially concerned with their respective environments, such as a “hostile environment.” As a result, there has been no clear discussion of how these concepts relate to each other and also to morally permissible and impermissible actions, which many times lead the discussion astray. In this paper I try to amend this by defining the central concepts, as well as showing how they relate to each other and morally permissible and impermissible actions.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.088 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".