A comparative study on parole as a post-conviction correction mechanism with special reference to Sri Lanka
Bibliographic record
Abstract
Parole, as an instrument of conditional release, has emerged as a critical post-conviction correction mechanism in modern criminal justice systems, aimed not only at reducing prisons’ overcrowding, but also at facilitating the rehabilitation and reintegration of prisoners into society to reach decreasing reconviction and recidivism. However, the efficacy of parole in achieving these objectives differs substantially across jurisdictions. This study adopts a doctrinal and comparative research method coupled with a critical analysis of the framework relating to parole systems in Sri Lanka. The study also examines parole in countries like Canada, UK and USA, in reducing reconviction and recidivism through the philosophy of rehabilitation and reintegration. The background to this study is rooted in the global shift from punitive incarceration to rehabilitative correctional models, which increasingly recognize the need for community reintegration. Despite this trend, Sri Lanka does not have a parole system and the prevailing early release systems employed by the Department of Prisons remain at an infant stage, lacking consistency, rehabilitative orientation, and procedural transparency. This study attempts to achieve four objectives: to assess parole concept in the criminal justice system; to examine the legal, procedural, and institutional framework governing parole in Sri Lanka; to compare this framework with the parole systems in Canada...
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".