Crime, Desire and Law’s Unconscious: Law, Literature and Culture, by David Gurnham
Bibliographic record
Abstract
S UNCONSCIOUS, David Gurnham deploys a Freudian model to explore the relationship between narrative and truth in legal judgments concerning sex, desire, and crime.Even though he acknowledges that "a factual enquiry after the event is itself a matter of narrative construction in the light of value judgments, and thus not at all simply 'factual,'" 2 it is not always clear whether the model deployed is meant to be heuristic or ontological.He says that psychoanalytical ideas are used "without any claim that [they] represent matters of scientific fact or … a priori truth" 3 and that "[r]eading law, literature and culture 'psychoanalytically' need not be a matter of imposing prefabricated structures of meaning, but of locating metaphors that offer alternative narratives and explanations."4 Despite these disclaimers, Gurnham reads judgments thematically and sees them as incorporating foundational concepts derived from Freud.To explain these concepts, he takes what he admits is "the thoroughly
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".