Bibliographic record
Abstract
This thesis focuses on spaces that have experienced brutal and radical transformations, in cities that have endured war, asking what architecture becomes when it collides with violence.While it dramatically alters architecture, destruction does not seem to belong to any architectural discourse.Hence, architecture as destruction becomes merely a product of violence.However, this research discusses destruction differently, it argues that when destruction impacts architecture, it should not be seen as a final act, rather architecture acquires new meanings.Analyzing the relationship between architecture and violence starts with investigating the human component in an attempt to explore a triangle between humans, violence, and architecture.How do perpetrators of violence carry out destruction without hesitation?Do they give up thinking and blindly follow orders?Is architecture often targeted because of what it represents?Does architecture contribute to empowering violence or resisting it?The research's intention resides not in presenting answers, but in reading and exploring some crucial ideas around the human experience of violence and its relation to architecture.It argues that architecture can transform violence as much as violence transforms architecture.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.059 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".