Encounters on the Fringe of a Museum Tour: Trailing Behind as a Site of Affective Intensities
Bibliographic record
Abstract
How can museum spaces contribute to our understanding of experience? While partnerships with community organizations have rectified critiques of the museum as an empty repository, understanding of what happens in this space from a first-person perspective has yet to keep up. For over 20 years, the Montreal Museum of Fine Arts has opened its doors, offering guided tours to persons living with Alzheimer's and their carers. This chapter draws upon an ethnography of these organized tours using a critical phenomenological lens which shifts attention on trailing behind as a site of affective intensity. We follow one participant trailing behind, tracing the emergence of significant experiences on the tour's margins. This focus raises questions about what may really matter and sharpens senses toward an aesthetic of the art encounter. Our critical phenomenological lens asks us to reconsider any typical categorical claims about the goods of opening up the museum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".