Fostering Radical Collaboration: The OCUL Collaborative Futures Project
Bibliographic record
Abstract
This paper was first given as a poster presentation at the Ontario Library Association Super Conference in 2016. Building on decades of successful cooperative work, the Ontario Council of University Libraries (OCUL) Collaborative Futures project aims to select and implement a shared next-generation library services platform (LSP), to manage and preserve print resources in a sustainable system, and to effectively and efficiently use a shared system for the management of electronic and print resources. Phase One of this project was completed in Summer 2015. This is its story. Cet article a été présenté pour la première fois comme une présentation d’affiches à la Super Conference de l’Association des bibliothèques de l’Ontario en 2016. Basant sur des décennies de collaboration réussie, le projet Collaborative Futures du Conseil des bibliothèques universitaires de l’Ontario vise à sélectionner et à mettre en oeuvre une plate-forme des services de bibliothèque de dernière génération, à gérer et à préserver des ressources imprimées dans un système viable, et à utiliser efficacement un système partagé pour la gestion des ressources imprimées et numériques. La première phase de ce projet a été complétée pendant l’été 2015. Ceci est son histoire.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".