Bibliographic record
Abstract
Within the history of penal systems, China represents a special case that initially adopted western penal models in the early 20th century but subsequently diverged onto a separate and unique path. Upon Mao Zedong’s victory in 1949 over the ruling Nationalist government, the focus on a western prison reform model shifted to one based on ideological purity and correct political thought, resulting in re-education camps known under the blanket term “laogai.” This change in penal practice occurred during the embryonic stages of the digital revolution, where humanity discovered ever more powerful methods of computation and data processing. The fixation on punishing incorrect thought led the People’s Republic of China (PRC) to adopt data collection and observation methodologies that could be easily digitized, allowing for exponential growth in oppressive systems. This comingling of laogai camp practices with computing power harnessed by the digital revolution has resulted in a new system of penal camps that is a stark break from traditional models. Using the PRC as a historical case study, this paper will explore the history of re-education camps, contextualizing the evolution towards the modern “smart camp” that is unique within the history of penology.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".