Bibliographic record
Abstract
Like modern baseball or hockey cards, tinsel prints were collectable images of famous actors and actresses produced in nineteenth century England. They could be purchased as singles or in sets of six or four, in colour or in black and white from. The purchaser could buy bags of prepared tinsel decorations along with the prints making them customizable. Thus, after the tinseling process, no two prints would be identical. This study focuses on the tinsel prints in the Robertson Davies Collection held at the W.D. Jordan Rare Books and Special Collections Library at Queen’s University. This research was conducted at the beginning of the campaign to conserve and rehouse the tinsel prints in from the Canadian author and playwright Roberston Davies’ personal collection. Each print was studied to determine the actor featured, the role they portray, the play this character is from, and the place each print has in the larger collection. The tinsel prints from the Robertson Davies Collection were also compared to tinsel prints in larger collections such as The Museum of London, The Victoria and Albert Museum, and The Folger Shakespeare Library, to assess the individuality of each print. The findings of this research formed the foundation of an Omeka based website to showcase the outcomes as well as high resolution pictures. This publically accessible platform allows for those outside the Queen’s community to explore the Robertson Davies Collection and further their own knowledge on 19th century English theatrical ephemera.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".