Diplomatics of born digital documents – considering documentary form in a digital environment
Bibliographic record
Abstract
Purpose – This paper aims to explore a new model of “record” that maps traditional attributes of a record onto a technical decomposition of digital records. It compares the core characteristics necessary to call a digital object a “record” in terms of diplomatics or “evidence” in terms of digital forensics. It then isolates three layers of abstraction: the conceptual, the logical and the physical. By identifying the essential elements of a record at each layer of abstraction, a diplomatics of digital records can be proposed. Design/methodology/approach – Digital diplomatics, a research outcome of the International Research on Permanent Authentic Records in Electronic Systems (InterPARES) project, gives archivists a methodology for analyzing the identity and integrity of digital records in electronic systems and thereby assessing their authenticity (Duranti and Preston, 2008; Duranti, 2005) and tracing their provenance. Findings – Digital records consist of user-generated data (content), system-generated metadata identifying source and location, application-generated metadata managing the look and performance of the record (e.g., native file format), application-generated metadata describing the data (e.g., file system metadata OS), and user-generated metadata describing the data. Digital diplomatics, based on a foundation of traditional diplomatic principles, can help identify digital records through their metadata and determine what metadata needs to be captured, managed and preserved. Originality/value – The value and originality of this paper is in the application of diplomatic principles to a deconstructed, technical view of digital records through functional metadata for assessing the identity and authenticity of digital records.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".