Internet Censorship: Access Issues for School Librarians in a Cyberspace World
Bibliographic record
Abstract
Converging communication technologies offer merizing potentialities for global access to local culture. However, concerns about controversial images and ideas on the Internet have inspired both political and technological challenges to open access. Over the past two or three years, a bewildering array of software products has appeared on the U.S. and Canadian markets that claim to be able to either "filter or 'rate' Intemet-based contenL Typical product claims are couched in the rhetoric of child protection and parental guidance. In the cyberspace universe of instant access to information and in ages of all kinds, how should school librarians around the world respond to these commercial products? How can they find a reasonable balance between the sometimes conflicting goals of parental responsibilities, children's educational and developmental interests, media literacy, and community standards? In view of what appears to be a growing political resolve in many countries to impose technological controls on Internet content, and a trend towards more and more labeling of creative expression in just about every other medium of communication, it is timely for librarians in all sectors to examine these issues and address the implications for information access. The topic of Internet filters is an exciting one for librarians because it represents the intersection of our roles as advocates for intellectual freedom, as organizers of infomation, and as promoters of media literacy. It gives us the opportunity to share our knowledge and expertise, and to increase our contribution to society at large and around the world.
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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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.023 | 0.025 |
| Scholarly communication | 0.047 | 0.051 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".