On Skin, Monsters and Boundaries: What <i>The Silence of the Lambs</i> can Teach Nurses About Abjection
Bibliographic record
Abstract
The 1991 film The Silence of the Lambs tracks the fictional pursuit of an American serial killer by a Federal Bureau of Investigation trainee, via the assistance of another incarcerated serial killer. It features psychologically disturbing themes, such as corpses, the mutilation of skin and monstrous persons. Incidentally, these are all themes regularly encountered by nurses in their day-to-day practices, including forensic mental health nurses. Despite regular encounters with these themes and phenomena, nurses continue to find them disturbing and troubling, but, at the same time, clinically fascinating. This paper will mobilize Kristeva's poststructuralist, psychoanalytic concept of abjection to relate the encounters in The Silence of the Lambs to those of nurses, to reconceptualize feelings of both disgust and fascination and to consider how vulnerability may benefit nursing practice. Of particular relevance are the breakdown of skin encountered in nursing practice, encounters with corpses and work with forensic patients considered monstrous. The film provides opportunity for nurses to conceptualize abjection in their own practice and to consider how a reconceptualization of boundaries and vulnerability may prove productive.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".