Can’t live without it: demystifying the private collector by unpacking the “Rob Brooks Mary Pickford Collection”
Bibliographic record
Abstract
Private collectors have a long history of generous donations to cultural heritage institutions, but donors and said institutions have had a contentious relationship. Both private collector and institution have a different relationship to the objects in the collection and this is reflected in the narratives attached to them, which can create tensions between the private collector and the public institution that accepts the donation. Film memorabilia collections and donations are subject to these very same tensions, but they have not been discussed at length in academic literature. This thesis examines the “Rob Brooks Mary Pickford Collection” at the TIFF Film Reference Library (FRL). It assesses the emotional narrative of the collector, Rob Brooks, who as a private collector gifted his collection, as well as the aims of the cultural institution, the narratives that are attached to the collection once it received, and how touring the collection may change that narrative.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.034 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".