Bibliographic record
Abstract
This contribution focuses on 'new narratives' dealing with the global issue of migration which however stands only as a paradigm of other forms of systemic injustice and discrimination. Taking paradigmatic projects -- Clouds over Sidra (2015) and This Room (2017) -- I will set off to look behind the promise of interactive, immersive narratives to let users 'walk in someone else's shoes'. This article explores in how far the specific affordances of VR affect the engagement with content and the potentially transformative impact the producers are aiming at. Are we dealing with an exploitive gaze, are we drawn into a 'human rights spectacle', or do new forms of narrative enable response-able witnessing? The theoretical framework brings together recent theories of VR non-fiction, drawing on the tradition of documentary theory and approaches to interactive storytelling, as well as findings in social psychology, especially conceptualizations of immersion, empathy, and presence in VR environments. Addressing problematic socio-cultural, socio-political and media-ethical constellations (the risk of 'improper distance', of dehistoricizing and depoliticizing complex issues, of reinscribing hegemonic points-of-view and of imposing one's own truth over the actual experiences of 'others', colonizing their feelings) I suggest a form of critical dis-immersion, arguing that the potential of new narratives does not consist in its amplification of visual illusion and immediate affective response but rather in its ability to model a different concept of subjectivity, questioning established regimes of gaze and perspective of the 'self' in relation to others.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".