Investigating the issue of copyright and security measures in digital libraries
Bibliographic record
Abstract
During the past few years, digital libraries have been the primary source of retrieving necessary information.IT helps many scholars have the access to recently published value added researches around the world.However, information security and copyright concerns are among the most important issues and there must be good rules and regulation to protect authors against any sort of copyright violation.In this paper, we present an empirical investigation to find out about the status of copyright issues in one of Iranian libraries.The proposed study of this paper designs a questionnaire in Likert scale and distributes it among 96 librarian experts.Cronbach alpha is equal to 0.76, which is well above the minimum acceptable level.The results of our investigation indicate that although expert believe the status of copyright is in desirable level when the level of significance is five percent, there are some concerns on some issues.In other words, experts believed that all copyrights are not well protected and digital libraries do not follow governmental rules and regulation on fully protecting authors' rights.In addition, experts believed that the security of sources available on digital libraries is not well protected.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".