A Research-co-Creation of Care: Feminist Speculation, Collaboration, and Curation in the Decameron 2.0 Virtual Gallery
Bibliographic record
Abstract
This paper presents the evolving work of the Decameron Collective, a group of women Canadian scholars and artists who, during the early COVID-19 pandemic (2020–2022), built a body of creative works as an interactive, virtual gallery of visual, audio, and textual media inspired by Giovanni Boccaccio’s Decameron (1353). More than a gloss on Boccaccio’s text, Decameron 2.0 is a feminist project of collaborative and curational worldbuilding. We describe our practice of co-creating and curating, making a case for interdisciplinary praxis-led approaches of research-creation that embody both a mode of inquiry and a practice of feminist ethics and care. We describe the world of Decameron 2.0 – its courtyards, caves, and rooms of spells, and the affordances of the webGL spatial navigation that contributes to the intermediality within and between the works. The intertextual spatialization of works in Decameron 2.0 and the non-linear exploratory affordances of the virtual environment extend into our academic thinking/theorizing through centripetal and centrifugal frames. In homage to the polyphony of mediaeval manuscript culture, and as an intervention against the flattening of so much academic discourse, we use illustrations, hyperlinks, margins, page layout, and metacommentary. This paratextuality illustrates what we call thinking-together and honours the differences among our individual voices and perspectives. This paper contributes to current critical and cultural understandings of making, care, communication, and feminist practice and models how multi-institutional collaboration can be reshaped in the era of and after COVID-19.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.097 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".