The Role of Prison Libraries in Normalization Process
Bibliographic record
Abstract
In the modern sense of punishment, notpunishing but restoring should be at the forefront. Thus, the aim is to help theprisoner overcome theperiod of incarceration with minimum damage and to transform this process into a constructive one as far as possible. At this point, the “normalization principle” is funda- mental which is adopted and emphasized by developed countries. The normalization principle requires the setting of conditions of the institution so as to resemble the regular circumstances in the society and the ways of life, to which the incarcerated are accustomed, as closely as possible. The hypothesis of this study is that a qulifiedprison library service will improve the normalization process in prison. Alt- hough prison libraries are negatively affected by the problems that prisons face and although security consideration creates barriers to the normalization process, prison libraries are the places where the incarceratedfeel free and closest to the outside world. In the world, especially in the USA, UK, Canada and Scandinavian countries, an effort to develop library services in prisons has been made since these are believed to contribute to the normalization process. Finally, the study indicates that prison libraries in Turkey don ’t receive enough support and that there are many deficiencies in this field. Hence the study concludes that many issues remain to be resolved in this regard.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".