Bibliographic record
Abstract
Economic recession, conditions of restricted spending and austerity politics have led architects to seek ways of expanding architecture and to a mainstreaming of ‘small’ architectures, small-scale designs and short-term interventions, many of which focus on contesting and remaking public space. Mass mobilizations of the past years, especially the protests of the so-called Arab spring, pose new opportunities for the field. This essay first frames the six case-based articles, included in this special issue, within the literature on the politics of public space and protest. It groups the case studies around two main categories of analysis: the transformative effect of mass protests on formal public spaces (walking) and the agency of protest occupations (camping). Second, the essay identifies a lack in literature of the role (or lack thereof) played by designers in contemporary mass mobilizations in the Islamic world. It further seeks to respond to that question by providing an overview of various approaches to social engagement in architectural research and practice, under the broad categories of ‘humanitarian design’ and ‘activism by design’ with an attention to the historical specificity of the Islamic world, the examples from which tend to be of the first, humanitarian, type.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".