Archival documents digitization policies: Developing a proposal checklist for documents digitization in Iranian archives
Bibliographic record
Abstract
Purpose: The present study aimed to review and compare the digitization policies of the documents existing in the national archives of the English-spoken countries (Australia, Malaysia, The United States, Canada, United Kingdom) if accessible as well as other available guidelines in this field, and to identify the common significant criteria and components in all the archives in order to develop the proposal checklist for digitization of the archival documents in Iran. Methodology: The library method was used to review the concepts through reading the texts and resources. Then, the proposal checklist was developed after studying the guidelines and principles of digitization of archival documents and websites of the national archives of the countries under study as well as corresponding with the related stakeholders and analyzing the contents of the digitization policies and finally identifying the criteria and components of common interest in all archives. Results: The findings showed that some of the criteria were mentioned in more than 70 percent of the studied guidelines and archives considered as the main criteria for developing a checklist for the archival documents digitization and included: project management, document selection, digitizing readiness, digitizing location and so on. Conclusion: A set of 14 criteria and 44 components identified through reviewing of digitizing policies of documents existing in the national archives of the studied countries and other available guidelines were used to develop the proposal checklist for digitization of the archival documents in Iran.
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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 | 0.121 | 0.182 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.010 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".