Bibliographic record
Abstract
Within the post-industrialized worldview, conventional correlations between a text and its linguistic determinations no longer hold as absolute, challenging the limits of a linguistic measure by semiotic analysis.Yet, even in the postmodern condition of a hyperreal realm where the visual image has replaced the literary sign as the predominant mode of global information and mass communication, the structuralist binary model of signifying semiotics comes under erasure.Taking Baudrillard's three orders of simulations as a ground, this study explores how the nature of his a-signifying model of semiosis confronts its own event horizon on the cinematic screen.Yet, where semiotics functions appropriately for the exegesis of text and image, those traces of human embodiment that remain discernable in film also appear to be under erasure.As a case study in the use of science fiction film for the examination of post-structuralist tools of cultural analysis, the limits of signifying signs become evident in the traces of embodiment evident in the film, I, Robot (2004), that was inspired by Isaac Asimov's 3 Laws of Robotics.As a challenge to the disembodied figures of contemporary science fiction, this study addresses what happens to embodiment under the orders of simulacra, to pose a transitional step in the movement from signifying semiotics to mimesis, via the a-signifying model of semiotic analysis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.025 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".