Monstrous Architecture and the Architect's Monster : Discovering Meaning in Architecture Through Critical Engagement and Intellectual Discourse
Bibliographic record
Abstract
The purpose of this thesis is to engage architecture, explore it through an imaginative process, with inventive forms of realism, and use architecture as a vehicle to engage design in a critical process. It will open the opportunity for discourse on the subject of monster. Not monsters under the bed or the ones hiding in the closet awaiting some unsuspecting child, but the breed that offers a discussion or commentary on a particular event, idea, or era. The monster is a way to demonstrate, "to show", and at the same time be explicit in meaning and representation. The Latin roots of the word monster links monstrum with monere, "to remind or warn", it is a "sign or an omen". It is said, that monsters are great signifiers, and in doing so, portray protagonists out of the ordinary. Literature and film are two very strong ways to employ the idea of monster as a narrative, and this thesis will reveal this tactic in architecture.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.094 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".