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Record W3140935690 · doi:10.29173/iasl7990

Censorship in Israeli High School Libraries: Analysis of Complaints and Librarians' Reactions

2021· article· en· W3140935690 on OpenAlexvenueno aff
Moshe Yitzhaki, Yosef Sharabi

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsCensorshipPornographyJudaismDenialPsychologyCriminologySociologySocial psychologyPolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

The study sought to determine the extent of censorship in high school libraries in Israel, using a questionnaire mailed to 442 schools and yielding 187 usable replies. Significant differences were found regarding both complaints about book content and librarians' response, between the religious sectors and the non-religious one, indicating a much lower rate in the latter. More complaints were received from teachers than principals, but the latter elicited a higher compliance, probably due to their special status in school. Very little parental involvement was reported, receiving the lowest compliance. Topics of the complaints ranked as follows: pornography, degrading the Jewish religion, hard violence, sexual permissiveness, missionizing, racism, drug use, holocaust denial and bizarre sects. Pornography and violence ranked high in all sectors, but sexual permissiveness and degrading Judaism ranked high among the two religious sectors but much lower in the nonreligious one. Few complaints about certain genres may result from preliminary censorship during book selection process, but in most cases it indicates less sensitivity in that sector concerning that genre.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.293
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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