Bibliographic record
Abstract
Named after the Ukrainian Orthodox prayer for the dead, the Canadian feminist Decameron Collective’s Memory Eternal (Вічная Пам'ять) is a single user a Virtual Reality work designed for Quest 2 that features seventeen distinct storytelling works which include 360 video, interactive and spatialized sound, AI generated images and video, film, as well as text. Set in a surreal and watery landscape, the works investigate our shared experiences of grief and loss at personal, societal, and planetary scales. The design of the world and the works in it center around a question: In the wake of crises, what do and should we remember, and how? Interactants’ journey through the world takes them through experiences of mourning, sitting with, and awakening to new futures. Research objectives: Memory Eternal was an experiment in finding interactive forms for grieving and memory work in response to personal losses, war, and the climate crisis. Speculative practices: The generation of the world and works in it is the product of a speculative feminist praxis asking what important relationships to the past and what forms of futurity can be attained through digital storytelling and research co-creation. The work results from years long conversations, inquiry, and artistic experimentation between the members of the Decameron Collective (Jolene Armstrong Kelly Egan, Lai-Tze Fan, Caitlin Fisher, Angela Joosse, Kari Maaren, Siobhan O’Flynn, Izabella Pruska-Oldenhof, and Monique Tschofen) anchored in an ethics of care. Documenting / bearing witness: Memory Eternal designs an experience that asks immersants to bear witness to individual grief, as well as participate in collective acts of grieving at a historical moment of overlapping personal and planetary crises. How can haptic and immersive interactive forms engage the thickness of lived histories? How can digital worlds create spaces of encounter that bring vastly diverse, heterogenous yet overlapping experiences together? Theoretical framework: The theoretical frameworks that informed the production and our current understanding of the work includes memory studies (Nora, Van Abbele, Young ); (an)archives and living archives (Springgay, Sabiescu, Siegel); autotheory (Fournier, Vanyecken, Strom, Puig de la Bellacasa); interactive documentary and co-creation (Gaudenzi, Cizek); care ethics; and research-creation (Springgay, Truman, Loveless). Methodology: The Decameron Collective’s methodologies have been informed more by research creation principles than by those of interactive documentary, digital design, film, or scholarship. Our work is emergent, dialogic, and collective. We begin with an intention, often thematic. Broader research questions emerge through the creation, and engagement with each other as co-creators, as well as with audiences and scholars. Emergence opens space for somatic thinking and knowing as legitimate forms of knowledge. It allows for the making of surprising connections, following threads we previously did not know existed, and experimenting with dimensional thinking. And emergence centre the dialogical. As such, emergence is more of a strategy than a method, a resistance to rigid methodologies that privileges interaction as opposed to results. We will be discussing both the possibilities and the intellectual and aesthetic risks of this methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.014 |
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".