Justice for crime victims: has the time finally come for a radical paradigm shift?
Bibliographic record
Abstract
How is it that punishment has become synonymous with justice and that justice has become a euphemism for punishment? How is it that justice and punishment have become almost interchangeable terms? How is it that slogans such as ?justice for victims? are invariably interpreted as demands for more and harsher punishments. Is punishment truly synonymous with justice? Are crime victims really bloodthirsty and totally obsessed with a quest for punishment? A troubling question that has haunted me throughout my professional life, a question that I spent many sleepless nights trying to find the answer to, is: How could it be that punishment, the deliberate infliction of pain and suffering, the deprivation of liberty, or deliberately putting a human being full of life to death be called or perceived as justice and be seen as the most appropriate response to victims? sufferings? How is it that victims who, as Nils Christie said, are the primary owners of the conflict (Christie, 1977) whose property rights were usurped, and whose rightful dues paid to them in the form of ?Wergeld? or the composition was expropriated by the state, were led to believe that justice is vengeance and retaliation and that the harsher the punishment, the more just is the judgment? How is it that the theological and abstract notions of retribution, expiation, atonement and penitence became so entrenched in people?s minds that no rational thinking, no scientific evidence, no economic crisis, no humanitarian endeavour seems to be capable of shaking such religious beliefs or lessening the incessant demands for punishment? How is it that punishment has become so universally accepted, extremely popular and so widely practised that people and governments, even in the harshest economic times, are more than willing to waste billions and billions of dollars for no other reason but to inflict pain and suffering on those fellow citizens who have violated man-made laws? The time has come to ask whether 21st century society has undergone a social, cultural and moral evolution that makes the retributive ?justice for victims? paradigm ripe for a radical shift? These are just some of the complex and difficult questions that are begging for answers and that will be examined in this paper.
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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 | 0.051 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.090 |
| Scholarly communication | 0.026 | 0.049 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.032 | 0.065 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".