Social, equitable, collaborative: 10 years of teaching and learning at the Sherman Centre for Digital Scholarship
Bibliographic record
Abstract
In the 10 years since its founding in 2012, the Lewis and Ruth Sherman Centre for Digital Scholarship has become a hub through which expertise and resources are shared with the campus community and anyone seeking to do more with digital scholarship. In this chapter, we explore how the centre has leveraged its involvement in teaching and learning to build a community to support the needs of researchers, mentor and develop the talents of emerging scholars, and produce unique programming to make learning about digital scholarship accessible. We share the core values of teaching and learning that guide programming, services, and activities at the centre, prioritizing the social before the technological, striving for equitability of access, and being collaborative by design. The goal of our chapter is to demonstrate how approaching teaching and learning in a way that prioritizes social connections and relationships, a critical engagement with digital technologies, and relational accountability has enabled the centre to iteratively build a reciprocal model of engagement that continues to adapt to the evolving role of digital scholarship in the McMaster community.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".