Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it