Across the Great Divide: Archival Discourse and the (Re)presentations of the Past in Late-Modern Society
Bibliographic record
Abstract
This article examines the implications for archival discourse in a late-modern world of technoculture featuring the digital re-mediation of documents, texts and images. Issues such as provenance, authority, memory and evidence are affected in a variety of ways once documents and photographs are detached from their original settings and enter cyberspace. Many theorists of the postmodern decry the effects of speed and time distortions associated with the age of the machine. Many also regret the passing of the autonomous, grounded individual characterized by an inherently stable self. This article suggests that there may be advantages to loosening some of the ties that bind us to traditional ways of looking at archival practices in late-modern society. RÉSUMÉCet article examine les implications, pour le discours archivistique dans notre monde de ce début du XXIe siècle, de la technoculture et de la reformulation numérique des documents, des textes et des images. Une fois ces objets détachés de leur contexte original et projetés dans le cyberespace, des questions comme la provenance, l'autorité, la mémoire et la preuve sont affectées de diverses façons. Plusieurs théoriciens du post-modernisme déplorent les effets de la distorsion du temps et de la vitesse associés à l'âge de la machine. Plusieurs regrettent aussi la fin de l'individu autonome et enraciné, caractérisé par un soi immanent et stable. L'auteure de cet article propose qu'un relâchement des liens qui nous unissent aux façons traditionnelles de voir les pratiques archivistiques peut comporter des avantages.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.033 | 0.084 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".