InterPARES 2 and the Records-Related Legislation of the European Union
Bibliographic record
Abstract
Ce texte fournit un aperçu de la législation, des politiques et des règlements de l'Union européenne par rapport aux exigences pour la préservation des documents numériques.Il sert aussi comme point d'entrée à un champ d'enquête sur lequel le projet InterPARES 2 s'est penché, c'est-à-dire l'étude des barrières et des outils pour la préservation des documents numériques qui peuvent se trouver dans la législation de bon nombre de pays dans le monde, y inclus l'Union européenne en tant qu'entité supra-nationale.ABSTRACT This article provides an overview of European Union legislation, policy, and regulations relating to the requirements for the preservation of digital records.It also serves as a window into one line of inquiry being undertaken in the InterPARES 2 project, that is, the study of the barriers and enablers to the preservation of digital records that may be found in the enabling legislation of a number of countries all over the world, including the European Union as a supranational entity.This article will focus on issues of digital preservation in the European Union (EU) legislation.Its aim is to identify weaknesses, either explicit or implicit, in current laws and regulations that both individual EU countries as well as the EU as a supranational body need to address in order to support digital preservation.The findings that are here presented are based on research conducted in the context of InterPARES 2, the second phase of the International Research on Permanent Authentic Records in Electronic Systems Project that was established with the objective of developing the
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".