An Archaeology of Mindhunting: Portraits of the Serial Profiler as a Figure of Reflexivity
Bibliographic record
Abstract
In the year 2017, two television series focused on pivotal moments in criminal psychology. In Mindhunter (Netflix), two pioneering agents from the FBI’s Behavioral Science Unit introduce new approaches to an emerging form of crime that defied categorisation, becoming the first “profilers” of “serial killers”. In Manhunt: Unabomber (Discovery Channel), another FBI agent develops a groundbreaking method of investigation based on idiolectal discursive patterns as revealed in the Unabomber’s Manifesto and correspondence, ultimately leading to the identification and capture of Theodore Kaczynski. Inspired by real people and events, the two series depict the heroic struggle of curious and unprejudiced police agents who face doubt and criticism, before eventually causing radical paradigm shifts. If their account of criminological innovation is unsurprisingly whiggish, the prominent role given to social sciences in both series is less conventional: disciplines such as comparative linguistics, critical theory or sociology of the deviance are not only instrumental in solving the crimes, but described as science in-the-making, showcased for their ability to make a difference in the world. Besides, as both series build upon previous representations of the profiler/serial-killer couple in popular fiction, they function as an archaeology of the genre itself. To what extent does this reflexive character of the shows can be compared to STS description practises? What do the series have to say about the role of language – recognized as a means of influence, used by the serial killers as well as by the investigators who resort to the same manipulative techniques to get information?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".