Bibliographic record
Abstract
In his 1927 “Archeion” article, On issues of modern Polish archival science (Z zagadnień nowożytnej archiwistyki polskiej), the renowned Polish archivist Kazimierz Konarski wrote of the challenge of managing the “shapeless mass” of modern archives in the 20th century. In this presentation, Canadian archival consultant and independent scholar Laura Millar examines the records and archives management challenge of the 21st century: managing the “shapeless mass” of electronic records inundating governments and organizations in the digital age. The “flood” of physical and textual documentation that Dr. K. Konarski faced a century ago has become a torrent of invisible, omnipresent, elusive electronic records – photographs, audio recordings, databases, AI-generated data, and more – stored in countless computer hard drives, cloud storage systems, and personal digital devices. How can the archivist manage digital sources that are both ephemeral and eternal at the same time? To ensure society has the documentary evidence it needs, L. Millar argues that archivists must our shift attention away from the care of static, “old” archives and focus more directly on the work of capturing and recording the present. The digital age may transform our methods, but our mission remains the same: to help society capture, protect, and make available for use essential sources of documentary proof.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.039 |
| Scholarly communication | 0.029 | 0.057 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".