The Digital Signature Dilemma: To Preserve or Not to Preserve
Bibliographic record
Abstract
Since the mid-1990s, dozens of States, including those of the EU, have reformed their evidence laws so as to grant digital signature technologies the same proof value as handwritten signatures, as a mechanism for proving identity of authorship, consentement to obligations, and integrity of electronic records after their transmission across time and space. Yet, several archival institutions (including the National Archives of Canada, Australia and France) have indicated they have no intention of preserving digitally signed records. This paper presents an overview of the development of digital signatures by the cryptographic research community, and the process of its legal codification as evidence of contractual relations. It argues that the process overlooked the problems induced by the need to preserve digital signatures over the long-term. It presents currently offered solutions to digital signature preservation, suggesting that they are in fact profoundly at odds with the principle of trusted custodianship at the heart of the archival profession.
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 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 | 0.031 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.015 | 0.036 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".