Bibliographic record
Abstract
The notion of authenticity has become a regular preoccupation within archival literature, and concern has been aired in numerous articles about its intention, achievability, or even relevance in a records management context. This article investigates these concerns by reconstructing the meaning of the word in relation to its original function through an evaluation of the impact of “memory,” “proof,” and “belief” on its veracity. Such investigations ultimately point to social construction as the most purposeful manifestation of authenticity, where the authentes record provides the truest of templates for this most polemic of attributes.RÉSUMÉ La notion de l’authenticité est devenue une préoccupation constante dans la littérature archivistique et de nombreux articles ont été consacrés à son intention, sa réalisation, voire même sa pertinence dans un contexte de gestion de documents. L’article explore ces questions en examinant le sens même du mot « authenticité » en lien avec sa fonction d’origine, à partir d’une évaluation des façons dont « la mémoire », « la preuve » et « les croyances » agissent sur sa véracité. De telles analyses concluent que la construction sociale est la manifestation la plus palpable de l’authenticité, où les documents authentes offrent le meilleur gabarit pour cette caractéristique polémique.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".