Bibliographic record
Abstract
The aim of this article is (1) to posit a conceptual model for the way ideas, conceptions, or feelings are represented or ‘figured’ in memory with the help of the imagination, and (2) to use this model to begin to outline what I believe constitutes part of our culture’s ‘memory-image’ of the serial killer in both fact and fiction. Human memory is not simply a passive storehouse of information. It is an active process whereby relations are created by way of the imagination. The ‘memory image’ is connected to what we wish to remember, but also to other images stored in memory, and inscribes itself in a vast ‘figural network’. I show how a given metaphor – ‘capitalism as cannibalism’ – can find its way in a given figural network, that of our memory-image of the serial killer. I investigate the rhetorical network that surrounds cannibalism and examine how this network offers our imagination a topos for our memory-image of the serial killer. Finally, I look at two films that activate this topos in their representation of serial killing, even though they avoid any direct thematization of it. The absence of any act of cannibalism in these films makes its ‘presence’ in our experience of them all the more compelling.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.149 | 0.016 |
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".