INTEGRATING FORENSIC AND ARCHIVISTIC FUNCTIONS: A POSSIBLE DIALOGUE
Bibliographic record
Abstract
Considering the persistent difficulties in managing digital archival documents, the studies developed in Archival Science establish relationships with other sciences to provide possible answers. Just as Diplomatic studies are relevant to Archivology in verifying authenticity through the application of external and internal elements in archival documents, forensic research is providing subsidies in administrative and archival routines, to try to achieve and guarantee documentary reliability (completeness, reliability, and authenticity). In this way, the objective of this article is to incorporate the concepts of Digital Forensic Science in Archival Science, work that is being developed in international projects, mainly in the United States and Canada, taking advantage of the maturity of forensic studies in the digital domain. Integrating the two sciences in question is reflected in the forensic functions: identification, compilation, preservation, verification, analysis, and presentation. Along with archival functions: creation/production, evaluation, classification, description, dissemination, preservation, and acquisition. For this, the methodology used is based on the scientific literature of national and international research in progress, which allows us to visualize both the convergences and the divergences of the forensic phases or steps, as well as the archival functions. The results pointed to the possibility of linking the functions of both sciences, reflecting, however, on some concepts that need to be better defined, in the search to establish a Forensic-Archival Science. The conclusion of this article is based on the need to continue delving into national studies, to delimit the elements and concepts that contribute to archival studies in digital reality.
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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 | 0.108 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.024 | 0.129 |
| Scholarly communication | 0.070 | 0.087 |
| Open science | 0.008 | 0.040 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 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".