Constellations of Community, Care, and Knowledge:A Collection of Vignettes from Pandemic Times
Bibliographic record
Abstract
Our Relational PracticeThis article brings together six graduate students from the 2020-2021 graduate residency program at the Lewis and Ruth Sherman Centre for Digital Scholarship-an annual, interdisciplinary program for emerging Digital Humanities (DH) scholars at McMaster University.Over the year of our residency, we found ourselves interrogating normative assumptions about and approaches to graduate training, mentorship, productivity, pedagogy, and public scholarship in DH.At the same time, our individual experiences of precarity and illness during a pandemic heightened the nefarious ways in which, in-and outside of academia, the colonial and the neoliberal abound.Our collective response to the pressures of both the pandemic and the university, however, centred on a deliberate and collaborative development of a community that privileged the networking of care and co-production of knowledge.The following six vignettes describe, honour, and extend our community practice as a means to reflect on the potentials of what we term intentional constellations of community in DH.By mobilizing a compilation of pieces rather than a unified authorial voice, we consider knowledge-making as a collaborative endeavour, in turn practicing relational methods to upend straight, colonial, and neoliberal modes of inquiry.Taken together, our interconnected vignettes enact a shared reflexive practice that we aim to centre in DH graduate training and mentorship.Established in 2012 as a collaboration between the University Library and the Faculty of Humanities, the Sherman Centre for Digital Scholarship (SCDS) offers technical and consultation services, workshops, and other learning opportunities that invite campus community members into digital scholarship approaches.As
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".