Data, Love, and Bodies: The Value of Privacy in Juli Zeh’s <i>Corpus Delicti</i>
Bibliographic record
Abstract
This essay explores the transformation processes depicted in Juli Zeh’s fictional narrative Corpus Delicti, which call attention to the ethical challenges of new forms of surveillance. Initially blinded by the ideology of the surveillance state of the Methode, the protagonist Mia Holl transforms from a supporter of the healthcare dictatorship into a member of the resistance. By focusing on the surveillance mechanisms of the Methode, Zeh’s fictional narrative opens up a discourse on the value of privacy in the information age. Read together with Roberto Simanowski’s Data Love, Zeh’s work allows for a re-evaluation of the sharing of personal data when it promises societal benefits. Mia Holl’s rediscovery of human nature as a love for oneself eventually has the power to challenge the legitimacy of the surveillance state. Through the unravelling of the Methode’s imposed “data love” as a mechanism of total control, Mia Holl is able to mentally liberate herself from the Methode’s ideology and spark a widespread protest against the state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".