Bibliographic record
Abstract
Exploring Reports of Recidivism by Guantánamo Bay Releasees. The purpose of this research is to examine what is known about recidivism by Guantánamo Bay releasees. Government reports suggest that approximately 27 percent of these releasees have returned to the battlefield while reporting in the open source media identifies the recidivism rate as nearly 9 percent. Deterrence, labeling and defiance theories were applied to explain their recidivism, and The New York Times’ Guantánamo Docket document release was used to code the 779 detainees on whether they were released, their nationality, age, time since release, risk level, intelligence value and other relevant domains. The recidivism data were obtained from the New America Foundation. These datasets were used to model the predictors of release from Guantánamo Bay and the predictors of recidivism for those who were released. Risk level, intelligence value, membership in multiple groups, and being of Yemeni nationality all statistically significantly affected the likelihood of release. However, only time since release predicted recidivism. It is likely that the proportion of detainees identified as recidivists will increase over time, as time to offend and be discovered increases, and as higher-risk detainees are released as part of the Obama Administration’s attempts to empty the island prison.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".