A Portrait of the Monster as Criminal, or the Criminal as Outcast: Opposing Aetiologies of Crime in Mary Shelley’s Frankenstein
Bibliographic record
Abstract
This article offers a criminological reading of Mary Shelley’s Frankenstein based on the 1831 edition. It discusses the opposition between Dr. Victor Frankenstein’s physiognomic prejudice and the creature’s discourse designating social exclusion as the cause of its mischief. Frankenstein’s accusations rely mostly on its creation’s appearance, borrowing from Johann Kaspar Lavater’s principles. The monstrous creature counteracts its maker’s presumptions by interpreting its own criminal behaviour similarly to Christian Wolf’s self-analysis in Schiller’s short story “Der Verbrecher aus Verlorene Ehre.” A close reading of the circumstances of each of the monster’s four crimes demonstrates how deeply its criminality is interlocked with social rejection caused by its own external deformity. Both perspectives adapt tropes that can be found in criminal biographies still reprinted in the 1810s. Though both positions are credible, I argue that the storyline supports the creature’s view that the criminal might be a monster, but created by those it vengefully hurts. Throughout, I indicate when changes to Shelley’s 1816-1817 draft were made to arrive to the 1831 wording, paying also attention to who effected them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 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".