Research, Collaboration, Experimentation: Creating a Digital Scholarship Unit in an Academic Library Setting
Bibliographic record
Abstract
Changes in traditional approaches to scholarship prompt new possibilities for the convergence of digital collections and scholarly communication. To meet the changing needs of its users, the University of Toronto Scarborough Library began a Digital Scholarship unit. The poster will describe both the rationale and practical considerations for establishing this unit.Des changements dans l’approche traditionnelle à l’enseignement donne lieu à de nouvelles occasions pour la convergence des collections numériques et la communication savante. Pour répondre aux besoins changeants de ses usagers, la bibliothèque du campus de Scarborough de l’Université de Toronto a mis sur pied une unité d’enseignement numérique. L’affiche présente les raisons et les considérations pratiques ayant mené à la création de l’unité.
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 | 0.101 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.057 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.006 | 0.045 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".