Authenticity Management in Long Term Digital Preservation of Medical Records: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
Managing authenticity is a crucial issue in the preservation of digital medical records, because of their legal value and of their relevance to the Scientific Community as experimental data.In order to assess the authenticity and the provenance of the records, one must be able to trace back, along the whole extent of their lifecycle since their creation, all the relevant events and transformations they have undergone and that may have affected their authenticity and provenance and collect the Preservation Description Information (PDI) as categorized by OAIS.This paper presents a model and a set of operational guidelines to collect and manage the authenticity evidence to properly document these transformations, that have been developed within the APARSEN project, a EU funded NoE, as an implementation of the InterPARES conceptual framework and of the CASPAR methodology.Moreover we discuss the implementation of the guidelines in a medical environment, the health care preservation repository in Vicenza Italy, where digital resources have a quite complex lifecycle including several changes of custody, aggregations and format migrations.The case study has proved the robustness of the methodology, which stands as a concrete proposal for a systematic and operational way to deal with the problem of authenticity management in complex environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.988 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".