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Record W2900994136 · doi:10.22215/etd/2016-11486

Architecture and Violence: Between Representation and Exchange

2016· dissertation· en· W2900994136 on OpenAlexaff
Jenan Ghazal

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsCarleton University
Fundersnot available
KeywordsArchitectureRepresentation (politics)Product (mathematics)Political scienceHistoryLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.399
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2016
Admission routes1
Has abstractyes

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