Reading Behind Bars: Exploring Reading Interests and Library Use of Prisoners in Croatian Correctional Facilities
Bibliographic record
Abstract
Abstract This chapter presents findings from a study into reading interests and habits of prisoners in six Croatian penitentiaries, and their perception and use of prison libraries. The study was conducted with the help of self-administered print survey. A total of 30% of prison population (male and female) in selected prisons was included in the study and a total of 504 valid questionnaires were returned (response rate of 81.3%). Findings indicate that reading is the respondents’ most popular leisure activity and that they read more now than before coming to prison. Respondents read more fiction than non-fiction. Most frequently they read crime novels, thrillers, and historical novels. To a lesser degree, they read religious literature, biographies, spiritual novels, social problem novels, self-help, war novels, science fiction, erotic novels, romances, spy novels and horrors. Respondents would like to read daily newspapers and magazines, and books about sport, health, travel, computers, hobbies, cookbooks, etc. Respondents have wide reading interests (both in relation to fiction and non-fiction) but they do not have access to them in their prison library. Respondents reported that reading makes their life in prison easier and their time in prison passes faster with books. Only about a quarter of respondents are satisfied with their prison library collection. Almost a fifth of respondents does not visit the library at all because it does not have anything they would like to find there: newspapers, modern literature, non-fiction, reading material for visually impaired and computers.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".