Bibliographic record
Abstract
In this paper, we discuss three alternative approaches to the dominant histories of techniques of illusion and interaction that emerged in the context of the panel “Alternative Beginnings: Towards an-Other history of immersive arts and technologies” sponsored by the New Media Caucus presented at the 2018 College Art Association Conference. Bringing together recent insights by media archaeologists (Huhtamo and Parikka 2011, Parikka 2012), decolonial thinkers (Mignolo 2011a, b), feminist and indigenous media scholars (Zylinska 2014, Todd 1996, Todd 2015) we invited papers that gave visibility to diverse genealogies of immersion, outside the dominant western art historical canon, to contextualize our current interest for embodied and multi-sensorial experiences. Focusing on the Latin American context – both geographically and epistemologically— the three critical approaches proposed include a discussion on the decolonizing potential of immersion as it moves away from a purely ocular regime towards an embodied one, an exploration of strategies that delink the development of immersive technologies from the military and for-profit game industry, and an emphasis on how localized sites can highlight the decolonizing potential of the local/global relationship in our possible rethinking of immersive technologies.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.038 | 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".