An Evidence-Based Approach to Advancing Open Science at the University of Ottawa
Bibliographic record
Abstract
In 2023, the University of Ottawa formed an Open Science Working Group charged with examining the state of open science at uOttawa, defining its goals towards open research practices, and recommending an action plan to position the university as an open science leader. The working group’s monthly meetings and final report were informed by a strong evidence base, including analyses of uOttawa’s research output, a review of national and international open science policies and practices, and estimates of expenditures on open access fees. This paper provides a detailed account of these processes and methods, providing insights for institutions seeking to develop their own open science strategies. Une approche fondée sur des données probantes pour faire avancer la science ouverte à l'Université d'Ottawa: les expériences du Groupe de travail sur la science ouverte RésuméEn 2023, l'uOttawa a formé un groupe de travail sur la science ouverte qui s'est chargé d'examiner l'état actuel de la science ouverte à l'uOttawa, de définir les objectifs de l'uOttawa en matière de pratiques de recherche ouverte et de recommander un plan d'action visant à positionner l'uOttawa en tant que chef de file en science ouverte. Les réunions mensuelles et le rapport final du groupe de travail se sont appuyés sur des données probantes comprenant des analyses de la production de recherche à l'uOttawa, des dépenses liées au libre accès ainsi que des politiques et des pratiques en science ouverte. Ce travail présente un compte rendu détaillé de ces processus et méthodes, qui peut être utile aux institutions qui souhaitent élaborer leurs propres stratégies en science ouverte. Mots-clésScience ouverte; libre accès; politique; groupe de travail; Canada; bilinguisme; francophonie
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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 | 0.263 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.027 | 0.024 |
| Science and technology studies | 0.026 | 0.033 |
| Scholarly communication | 0.049 | 0.021 |
| Open science | 0.014 | 0.029 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".