Bibliographic record
Abstract
This chapter is the most innovative of the case studies in that it examines 2167 (2017), an art installation consisting of four immersive virtual realities (VR) that represent different versions of an Indigenous future. The analysis first looks at the VRs that are more easily accessible since they were created from filmic material, Honour Dance by Kent Monkman and The Hunt by Danis Goulet, and then analyzes the VRs that were created entirely with VR-creation software and show only digital shapes and landscapes, Blueberry Pie Under a Martian Sky by Scott Benesiinaabandan and Each Branch Determined by Postcommodity. All in all, the VRs interpret the project’s anti-colonial goal, to look 150 years into the Indigenous future instead of celebrating Canada’s colonial beginnings 150 years in the past, by imagining worlds beyond the colonial nation-state. As most readers will not be familiar with the project, this chapter also contains descriptions of the different VRs, briefly zooms in on the technological aspects and the general walkthrough experience, and closes on a short discussion of the advantages and disadvantages of the innovative medium.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".