Defining Electronic Series: A Study
Bibliographic record
Abstract
Cet article explore la façon dont les séries de documents électroniques opérationnels, c'est-à-dire les séries de documents telles qu'elles furent créées, conservées et utilisées par les créateurs au cours de leurs activités, sont transformées par le processus d'acquisition en séries archivistiques.L'auteur soutient que les postulats qui sous-tendent le processus d'acquisition et qui furent développées dans le contexte d'un environnement papier auraient besoin d'être réexaminées afin d'être utilisées dans un environnement électronique en s'appuyant sur des exemples tirés de ses récentes expériences aux Archives de l'Ontario.L'article se termine sur des observations quant à l'impact de l'acquisition de séries de documents électroniques pour les institutions d'archives.ABSTRACT This article explores the way operational electronic series, i.e., series of records as they were created, maintained, and used by creators during their normal course of business, are transformed into archival series by the acquisition process.It contends that the assumptions behind acquisition processes developed in the paper environment may need re-examination for use in the electronic environment, using illustrations drawn from recent experiences at the Archives of Ontario.The article closes with some observations on the impact of acquiring electronic series on archival institutions.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".