Bibliographic record
Abstract
The 2010 novel Enlèvement avec rançon by Yves Ravey is, perhaps unwittingly, a case study in obsessional neurosis. In this concise work, the reader is guided through the twists and turns of a crime committed by the narrator, Max, who has recruited his older brother to assist him in kidnapping the daughter of his boss at the sheet-metal business where he is an accountant. Despite its resemblance to a crime novel, the narrative is focussed primarily on the knot of familial and professional relations which give rise to the kidnapping plan. Within the theoretical framework of psychoanalysis, I demonstrate that Max’s enigmatic and often contradictory motivations – to get rich, betray his brother, seduce the kidnapping victim – can be explained with reference to Lacan’s extension of Freud’s work on obsessional neurosis. Lacan, in highlighting the patrocentric nature of this psychological structure, shows that obsession invariably takes the form of a jouissance, a self-destructive behaviour in which the subject takes an unconscious pleasure in his betrayal by the father, and is thereby removed, enlevé (kidnapped), from the chain of desire. More broadly, Ravey’s novel, like his other narratives in which the father’s role in family power relations is put into question, points to a crisis of modernity in which the subject, lacking the law provided by the father yet paradoxically still under its sway, seeks consolation in compulsive rituals.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".