Digital Humanities and Renaissance Studies in Canada: A Graduate Student’s Perspective
Bibliographic record
Abstract
This article focuses on digital humanities and Renaissance studies in Canada, highlighting established projects such as Iter and newer efforts such as Serai, and addressing recent interest in historical GIS. This survey of projects demonstrates how the work of Renaissance studies faculty and graduate students in Canada is increasing accessibility to sources, creating new knowledge environments and spaces for collaboration, and encouraging new ways to map and visualize Renaissance data, with an end result that enhances our understanding of the past and the ways that digital technology is changing humanities scholarship. The article also suggests that from the perspective of graduate students, participation in these endeavours provides not only training in digital technologies but also the opportunity to contribute knowledge to the field in concrete ways and the chance to establish a foundation in methodologies and practices that will shape approaches to Renaissance studies research and teaching in the future. Cet article se penche sur les humanités numériques et les études de la Renaissance au Canada, en présentant des projets établis tels qu’Iter et plus récents tels que Serai, ainsi qu’en examinant l’intérêt plus récent pour le système d’information géographique (SIG) historique. Ce survol de différents projets montre comment le travail de professeurs et d’étudiants aux études supérieures dans le domaine améliore l’accès aux sources, créent des environnements pour de nouvelles connaissances et des espaces de collaboration, et favorisent de nouvelles façons de visualiser des données relatives à la Renaissance, enrichissant ainsi notre compréhension du passé, tout en mettant en lumière les transformations des sciences humaines provoquées par les technologies numériques. Cet article avance également qu’en ce qui concerne les étudiants aux études supérieures, la participation dans ces projets non seulement leur donne de l’expérience en humanités numériques, mais leur donne aussi la chance de pouvoir contribuer de façon concrète à l’avancement des connaissances dans leur domaine. Ces expériences leur donne également l’opportunité de développer une méthode et des pratiques qui détermineront leurs approches dans leur recherche et leur enseignement à venir en études de la Renaissance.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.033 | 0.022 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".