Migration at Scale: A Case Study: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
Increasing experience in developing and maintaining large repositories of digital objects suggests that changes in the largescale infrastructure of archives, their capabilities, and their communities of use, will themselves necessitate the ability to manage, manipulate, move, and migrate content at very large scales.Migration at scale of digital assets, whether those assets are deposited with the archive, or are created as preservation system artifacts by the archive, and whether migration is employed as a strategy for managing the risk of format obsolescence, for repository management, or for other reasons, is a challenge facing many large-scale digital archives and repositories.This paper explores the experience of Portico (www.portico.org),a not-for-profit digital preservation service providing a permanent archive of electronic journals, books, and other scholarly content, as it undertook a migration of the XML files that document the descriptive, technical, events, and structural metadata for approximately 15 million e-journal articles in its archive.It describes the purpose, planning, technical challenges, and quality assurance demands associated with digital object migration at very large scales.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.860 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".