Fiction as Pedagogy: Toward a De-Anthropocentric Architectural Education
Bibliographic record
Abstract
Critiquing the anthropocentric dispositions of architectural education, this dissertation introduces a "de-anthropocentric" vector of ethical thinking through fiction as a form of pedagogy. The term de-anthropocentric as opposed to non-anthropocentric here posits nonhuman life as an important dimension of architectural consideration while acknowledging that there are limitations to understanding or advocating on behalf of the nonhuman other. By problematizing anthropocentrism in this way, the research participates in concurrent discourses in philosophy, education theory, anthropology, biology and literature studies that challenge the inherited biases of Western ontology and epistemology. Recognizing the predominance of education in structuring these biases, the research takes inspiration from experimental approaches in posthuman education studies that historically situate and reorient definitions of the human and disciplinarity. Toward this, the dissertation investigates three trajectories in literature studies as departure points: the weird realism of Howard Phillips Lovecraft, the multispecies worlding of Donna Haraway and the graphic portrayal of animal subjectivities in David Herman's narratology beyond the human. From these examples, the dissertation theorizes nonhuman narrative, representation and worldbuilding approaches in an architectural context. Finally, locating the early Renaissance as a period of major educational transition in architecture, the research analyzes Antonio di Pietro Averlino's Libro architettonico (1461-63) as a model of fiction-based pedagogy for the present. Written as a My sincerest appreciation goes to Stephen Fai for continually reminding me what this dissertation was trying to do and helping to achieve it. I am indebted to him for the many opportunities he has provided throughout my academic career. Claudio Sgarbi has been a source of deep insight and necessary interrogation throughout the years. I owe him immeasurable gratitude for his encouragement and for all the tunnels and burrows
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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 teacher head, 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".