Bibliographic record
Abstract
Digital Fiction Curios is a prototype immersive gallery that experiments with the concept of celebrating and exploring older electronic literature through virtual reality (VR).Many of Dreaming Methods' earliest stories were created in Flash, a technology that was removed from all major web browsers in 2020.Digital Fiction Curios archives and re-purposes three of our Flash works originally made as far back as 1999 and makes it possible to explore them in VR.As a reader/player, Curios places you inside a mysterious "curiosity shop" where all manner of historical items and gadgets can be picked up and examined.Almost everything on show was created digitally decades ago.Curios was made working with Alice Bell from Sheffield Hallam University, funded through the university's Creating Knowledge Impact Acceleration Account.The music is by Chris Joseph.Digital Fiction Curios had an extremely low budget for a VR experience and was challenging to create.It was entirely built in-house by Dreaming Methods as an attempt to offer an authentic window into three original Flash works, presenting them in a new and unique way that would invite close examination, supporting research materials and the opportunity to partly re-imagine the works as if they had been created for VR itself.Our eventual hope was then to look at creating a wider archive of digital fiction by other artists/writers.The process involved discovering a way to run Flash content within VR and to allow that content to be interacted with.Our work in this area was limited to PCs and Windows specifically.The project was co-curated
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".