Censorship in High School Libraries in Israel: An Exploratory Field Study
Bibliographic record
Abstract
The issue of censorship in school libraries has long concerned researchers and practitioners in the field and is well documented in literature. A literature survey reveals dozens of opinion articles as well as research papers reporting empirical field studies, especially in the US, which attempt to determine the extent and objects of censorship and its underlying factors. Although school libraries have existed in Israel for several decades, some of them dating back to the 50's, the issue of censorship has not been studied. The objective of the present exploratory study was to empirically assess the current state of censorship in a sample of high school libraries in the country. The sample comprised 25 high schools, from both the 'religious' and 'non-religious' sectors. The main research tool was a two-parts questionnaire, designed to be filled out during researchers visit to the library. Most librarians reported that some form of control, restriction or censorship was, always or very often, applied to books to be added to the collection, usually by the head librarian. No library had any kind of written document, or even some permanent unwritten statement specifying instructions or policy regarding book acquisition. None of the librarians mentioned any external pressure to censor the existing collection or books to be acquired, and it was dear that the only censorship was an internal one, initiated by the librarians themselves and other members of the school staff. In both sectors there was wide agreement on banning titles which included violence, hard pomography, racism and drugs, but there were considerable differences regarding 'soft' pornography, Holocaust denial, sexual pemissiveness and books degrading the Jewish religion or its values, principles or commands. A comparison of four lists of specific titles against the catalogs showed that the `non-religious' libraries owned three times the percentage from the first list (explicit violence and sex), but only half the percentage from the fourth list ('Judaism), compared to the 'religious' one. Interestingly, the religious group had lower percentages of both the 'rightist' and 'leftist' lists, due to its smaller collections, but relatively more from the 'rightist' list than the 'leftist' one.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".