A global view on digital legal deposit: what could be learned?
Bibliographic record
Abstract
Aim Procedures for legal deposit of the print material are well established and widely practiced. Such procedures, however, are not necessarily applicable to the digital environment, mainly because bibliographic specifications of print material do not match those of digital objects and digital environment. The history of legal deposit of publications in Iran goes back to 1906. Since then, the press, publishers, public and private institutions are required to deposit one or more copies of their published material to the National Library of Iran (NLIR). However, reports have always indicated incomplete deposit or lack of coherent and comprehensive policies, especially with regard to digital resources. The present study aims to propose a framework for NLIR by comparing several national libraries. Methodology This research examined the policies and practices of 10 large national libraries that according to the World atlas (2018) have been ranked the highest in terms of the number and variety of resources. They were the Library of Congress, the British Library, National Library and Archives of Canada (LAC), and national libraries of France, Germany, Denmark, Spain, Australia, Japan, and China. The data related to digital legal deposit implementation was collected from various sources including directories; official publications of international organizations, national legal deposit laws and regulations. Findings A global trend towards legal deposit to cover digital publications and preservation of comprehensive national archives is developing. In Iran, there was no mechanisms for identification, selection and deposit of digital materials either do not exist or are inappropriate for some types of publications. Conclusion National libraries and other libraries are at the forefront of research and development in the field of legal deposit. In this research, an effort was made to design and propose a framework for depositing digital material at NLIR drawing on the policies and practices of the top 10 national libraries. The proposed framework has two parts. The first includes elements such as introduction, general principles, resources comprehensiveness, legal deposit method, the origin of publications, collaboration and partnership, and legal considerations. In the second part -the plan-areas addressed in the first part are discussed in detail.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.023 | 0.067 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".