Bibliographic record
Abstract
Purpose – This paper aims to present a conceptual methodology, named herein as object-oriented diplomatics, based on a presentation given by the author at the Digital Diplomatics 2014 conference. This methodology centers on building digital records capable of supporting their authenticity over time and when removed from their original systems by extending archival diplomatics theory by leveraging object-oriented programming (OOP) principles. Design/methodology/approach – This paper presents new method for supporting the presumption of authenticity of digital records through extending archival diplomatics concepts into OOP principles when creating digital records within a record-keeping system. Findings – This paper is based on a preliminary research being conducted during the design of a government digital archives. This concept was used as a core design element for their digital archives and has thus far shown great promise in articulating and encapsulating those essential data elements that support the presumption of authenticity across a wide diversity of record types. Originality/value – This paper presents a new approach to support the presumption of authenticity of digital records by utilizing concepts from archival diplomatics leveraged with OOP principles. It is the hope of the author that this paper will initiate a deeper collaboration between archives and records management professionals and software developers in the design and implementation of digital repositories.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".