Building Community and Collaboration Through the Digital Humanities Toolbox Series
Bibliographic record
Abstract
The University of Ottawa has a long history of digital humanities scholarship and teaching. What began as a small number of humanities and social science researchers using digital methods in their work has grown to a larger and more formalized community of scholars over the years. Coinciding with a new minor in digital humanities, the DH Toolbox series was launched by the Library in Fall 2017. This bi-weekly series aims to create a space where researchers can network, learn about new cutting-edge techniques and methods, and discover research tools. Now coordinated through the faculty, DH Toolbox offers a “way in” and opportunity to build community, to share work and exchange feedback. Though originally intended to support researchers at the university, one of the unexpected outcomes of the DH Toolbox series was community engagement beyond the campus. Over the course of the first four years, researchers and students from other institutions in the region, from the GLAM sector, and arts organizations have participated both as attendees and presenters. These workshops have become a hub of regional community engagement and have served as a launching pad for multi-institution research and teaching collaborations. While we have had some great successes with the workshop series, the one-off model that seeks to serve a variety of learners comes with many challenges. Not only do our participants come to the sessions with varying levels of digital expertise, but they do so from a variety of disciplinary backgrounds that can make effective workshop delivery challenging. This chapter will reflect on the benefits and challenges that have arisen with developing the DH Toolbox series, and address the creative approaches required in order to address learning gaps, maintain student and faculty engagement, and ensure that sessions are attracting a diverse range of presenters and participants.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".