Return of the Cabinet of Curiosities: A Tour of the Niagara Falls Museum
Bibliographic record
Abstract
I have a vivid recollection of my first visit to a “real” museum – the Royal Ontario Museum – when I was somewhere around ten years old. I was awestruck at the sight of glass-cased stuffed leopards, open-casketed mummies, massive Asian stone carvings and towering dinosaur skeletons in (to my eyes) cavernous halls. Well, of course. It was my first experience of the sublime. Three impressions stand out now, however, with the passage of time and the changes we have all seen in museum culture. The first is that, as I remember, we touched the bones of the dinosaur, clambered over the Asian stone statuary, roamed freely among and between the exhibits in what seems now either curatorial sacrilege or the roots of “please touch” exhibits. I may misremember, but I have it in my head that a large part of my wonder was predicated on being able to use my sense of touch in a profligate manner throughout the museum. I recollect, as well, being overwhelmed by the number of objects on display, in a disordered chaos of materiality – a Noah’s ark of artefacts. No doubt there was a structure, room by room, and there were descriptive labels; but my child’s-eye view received it otherwise. There was, finally, a deep sense of wonder at the age of things – and a knowledge that these “things” were indeed “old,” reinforced and given credence by the fact that they were covered with a layer of dust. They must have been of great age (I thought) because they had been in this ancient building for so long, unmoved and unchanging. I had, then, at the age of ten, in every respect confused the setting with the artefact. This is evidence of my own naivete, though I suspect the error is entirely normal.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.026 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".