An Overview of Digital Preservation Considerations for Production of “Preservable” e-Records: An Indian e-Government Case Study: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
In the Indian context, when the e-government records are received for archival purpose, it is observed that very often they are produced without proper compliances for long term digital preservation.This paper presents a case study of e-district Mission Mode Project which offers diverse citizen services and produces the e-records such as birth certificates, domicile certificates, marriage certificates, caste certificates, etc in very large volumes.Such born digital e-government records have to be retained and preserved for technological and legal reasons.The Centre of Excellence for Digital Preservation established at C-DAC, Pune, India has carried out the study of e-record production process in the e-district and the need analysis for its digital preservation.The digital preservation best practices are identified, which have to be incorporated in the production process of erecords, so that the final e-records are produced in "preservable" form with full compliance as per the requirements of OAIS.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".