Our Home and Haunted Land \nAn Exploration of Space, Memory and Virtual Reality
Bibliographic record
Abstract
Our Home and Haunted Land: An Exploration of Space, Memory and Virtual Reality \nis a linear virtual reality experience that examines the colonial histories embedded in the names of a number of Toronto spaces. It incorporates research-creation and decolonial methodologies to expose the violent legacy of the land with live on. The project uses virtual reality as a means to challenge what Sara Ahmed calls the “whiteness” of our societal and physical space by reinserting a simultaneously historical and futuristic Blackness in profound allyship with Indigeneity. \nThe project explores the following questions: how can immersive digital storytelling be used to decolonize space and embody history and memory? How can virtual space be used to visualize the cultural and historical relationship between physical spaces? The following tensions are at the core of this project: an “invisibilized” history that permeates the construction of our society; names that allude to a past that remains largely unknown; counter-archives that seek to tell stories that challenge the dominant history.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".