Bibliographic record
Abstract
Ever since its much noticed use in the 1973 film Westworld (Michael Crichton, USA, 1973) CGI (computer-generated imagery) has been continuously altering cinematic perception of reality. One of the major changes concerns the production of space. Though CGI is regularly employed to create fantasy worlds or utopian landscapes, it is worth noting that more and more filmmakers are turning to it in order to produce contemporary landscapes of devastation brought about by an atomic, military, or environmental catastrophe. Films such as Wall e (Andrew Stanton, USA, 2008) or 9 (Shane Acker, USA, 2009) are symptomatic in this respect of the aesthetic importance filmmakers now attach to the use of CGI for the representation of devastation. This phenomenon, which can be described here as a “new aesthetic of disaster,” leads us to examine the concept of “Traumascape” in connection with current digital culture, and more particularly in relation to the cinematic “virtualization” of spatial reality. In our view, this “virtualization” allows for a visual “exponentiation” of said reality, thus making it ascend to the power of the “Traumatic Real” in which originates the enigmatic sublimeness of space. Generally speaking, our article intends to analyse the production of digital traumatic space in cinema and to demonstrate its novel relationship with the sublime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 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".