Bibliographic record
Abstract
This paper examines several narratives of techno-horror in literature and film. Special attention is paid to the recurring trope of monstrosity arising from a technologically augmented sense of sight. Utilizing a psychoanalytically informed analysis, this paper argues that fictions can express latent, untenable dimensions of very real experiences. In the case of techno-horror, narratives of sight, imagination, and projection-made-monstrous are rooted in contemporary relationships with technology and its capacity for depicting and transmitting unconscious fantasies. In this relationship, the technological is the extension of a tangible category of humanity, while nevertheless containing the fear that this extension dissolves its stability. Thus, the genre of techno-horror is unique in expressing the role of unconscious fantasies – our unattainable ideals for becoming “prosthetic Gods,” as Freud put it (1930) – in our relationship with technology. Like the ideal of transcendence in religion, this technological ideal is a desire for both an impossible future, as well as the wish to return to an equally impossible, infantile past. Ultimately, this paper suggests that techno-horror narratives are expressions of a failure in taking responsibility for the othered unconscious fantasies that motivate our relationship with technology. Understanding these narratives within the context of psychoanalytic projection and situating them within the long tradition of imagining a transcendence of the human subject affords a better understanding of the cultural work accomplished by these contemporary expressions of the human-made-monstrous.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".