Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article analyses two late 19th-century technologies that sought to identify criminals with scientific accuracy: Alphonse Bertillon's techniques for measuring bodies and Francis Galton's composite portraits of criminal types. It analyzes the regulatory environment in England in which their ideas achieved considerable prominence, emphasizing crucial differences in the visions of `science' embraced by the two men. By highlighting their different claims to science, this article outlines their respective legacies in criminal identification arenas, and isolates unique dangers associated with each. Those dangers variously alert us to the importance of questioning a persistent, if often implicit, sentiment within much criminal justice thinking; namely, by identifying individual criminals, or criminal types, justice systems effectively address the problem of crime. Against this approach, and resonant with a critical criminology in search of less exclusionary ways to govern, the following analysis considers criminal identification not as a discovery but a creation. In so doing, it seeks to re-politicize current practices of criminalization, challenge claims to the purported scientific impartiality of criminal identification and embrace the possibility of justice beyond a dominant reflex to create criminals.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it