Finding Wolff: Intellectually Arranging the Werner Wolff Fonds at the Ryerson Image Centre
Bibliographic record
Abstract
This thesis presents the results of an applied project in Collections Management, comprising the intellectual arrangement of the Werner Wolff fonds at the Ryerson Image Centre (RIC), and the creation of a finding aid to promote public research in the collection. Wolff was a photojournalist from the late 1930s to the 1980s who amassed a collection of over 1,300 files of photographs and related materials, which his son donated to the RIC in 2009. The project revolved around the organization of the collection inventory, a necessary step before it was possible to proceed with the intellectual arrangement of the fonds into series and sub-series with descriptions of each as set by the Canadian standard Rules for Archival Description. This thesis describes the arrangement of the fonds and the decisions made along the way. It also includes a copy of the finding aid written to promote public access to the fonds. The conclusion outlines the future needs of the collection to finalize the processing of the fonds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".