Punitive Turn or Punitive Imperialism? Analyzing the Transformation in the Ecuadorian Penal Realm
Bibliographic record
Abstract
Abstract Criminological research, particularly in the Anglo-Saxon academic realm, has extensively examined the sharp increase in incarceration rates since the mid-1970s. Referred to as the “sociologies of the punitive turn” (Carrier, 2010), these studies argue that this surge reflects a sudden and harsh transformation in the logic governing penal practices and discourse. Some findings even suggest that this punitive shift has a global reach, impacting regions like Latin America. This broader narrative prompts an inquiry into whether a similar punitive turn occurred in Ecuador, a South American nation. Examination of prison demographics and legal frameworks in this country reveals a notable increase in incarceration rates during the 1990s, closely linked to drug trafficking control policies led by the United States. Consequently, I suggest that while the influence of neoliberal rationality, characteristic of the punitive turn, is evident, it’s more aptly described as a manifestation of punitive imperialism. Thus, it is imperative to analyze shifts in punishment trends within the framework of imperial dynamics, particularly considering the economic dependency of peripheral countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".