SUZANNE CONKLIN AKBARI, Seeing Through the Veil. Optical Theory and Medieval Allegory. Pp. x +354. Toronto, Buffalo, London: University of Toronto Press, 2004. 42.00 (ISBN 0 8020 3605 8)
Bibliographic record
Abstract
SO CRUCIAL is light to most of human experience that the recurrent literary use of metaphor joining vision and knowledge has always been commonplace. The strength and character of the metaphor, however, has varied greatly over time, and generally speaking we do not expect to find high science in the background to it. In examining various medieval allegories, including the Roman de la rose, and works by Dante and Chaucer, Suzanne Akbari maintains that it is possible to detect in them signs of influence from medieval theories of optics. She covers her ground in a very thorough fashion, beginning with Plato and Aristotle, and much other classical opinion on the linking of visual experience with intellectual reasoning, imagination (in its various senses), judgement, memory, and divine revelation. Surveying recent literature on allegory, she distinguishes between its broad division into theories of iconography and theories of rhetoric, allegory as image and allegory as trope; but her opening chapter – a masterpiece of compression – makes it clear that her own approach will uncover only a part of what can and should be said about allegory. Following on with a brief survey of optical theories that she considers relevant to medieval literature, she introduces us to the thesis that will run through later chapters, which is that whereas in the earlier middle ages vision was a mediator between subject and object, the perspectivist optics of the later period introduced ‘point of view’ into visual imagery, and that the stress placed on the fundamentally analogical nature of all knowledge was reflected in the literature of the time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".