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Record W4410316361 · doi:10.32920/ifmj.v4i1-2.1987

Emergence as Method

2024· article· en· W4410316361 on OpenAlexaffvenueabout
Monique Tschofen, Jolene Armstrong

Bibliographic record

VenueInteractive Film and Media Journal · 2024
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsAthabasca UniversityToronto Metropolitan University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.011
Scholarly communication0.0120.008
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0780.014

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.

Opus teacher head0.059
GPT teacher head0.490
Teacher spread0.431 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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