The Spectrum of Defensive Placemaking: Affordances and Perceptibility in Public Spaces of Leisure
Bibliographic record
Abstract
Defensive architecture refers to strategic use of design elements to limit access and to modify behavior by changing the affordances of (public) spaces. This practice often disproportionately affects marginalized communities like underhoused individuals or youth subcultures by influencing their modes of participation in public life of cities. In this way, defensive architecture effectively shapes ingroups, outgroups, and the possible and permissible behaviors in spaces of leisure. This conceptual paper situates defensive architecture within the broader construct of placemaking in urban contexts and, using concrete examples, constructs a spectrum of perceptibility for defensive placemaking ranging from overt to covert. Such measures and urban inhabitants’ subjective understandings of and interactions with them add a layer of fluidity to the differentiation between ingroups and outgroups. Building on this multilayered nature of defensive placemaking, the paper calls for a holistic approach in evaluating its role in leisure settings, and advocates for a paradigm shift from defensive to adaptive placemaking in which the design focus shifts from rigid design elements with high exclusionary potentials toward prioritizing multiple affordances that foster diverse uses and user groups. In this context, implications for practitioners and policymakers as well as future research directions for leisure scholars are introduced.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".