At Play in the Fields of Pattern: Theorizing Pattern Thinking in a Museum of Islamic Art
Bibliographic record
Abstract
This dissertation examines the interaction between a museum-going subject and a patterned museum object from two perspectives: scholarly writing about pattern and the experiences of visitors to the Aga Khan Museum in Toronto, Canada. Patterning is fundamental to human meaning-making, but in Euro-American theories of art, especially in categories of decorative art or ornament, it tends to be overlooked and under-theorized. In museology, visitors are rarely if ever asked about their responses to the patterned objects that they view. I combine situational analysis methodologies with digital humanities methods of data mining and data visualizations to compare my findings from my interviews with AKM visitors to scholarly writing about pattern. My argument arising from the comparison is that visual patterns on objects do not fulfil a mere decorative function, but have a narrative power that moves in the space between object and subject via their unique histories, interacting with the subjects histories and prior experiences to produce meaning that is situated, contingent, and embodied. Specifically, I highlight visual patterns on objects as transdiscursive, a term which describes their paradoxical nature as signifiers of meaning. I argue that they are fixed and fluid at the same time: fixed to the technical properties of their objects, but apt to appear on objects spanning many geographies and time periods. By approaching them in this way, I assign new prominence to patterned objects as conveyors of stories in museum gallery viewing. Finally, beyond this study, the methodological pairing of situational analysis and data mining that produced my new understanding of patterns has possibilities for future research beyond museology and pattern studies to pursue a broader set of questions.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".