Bibliographic record
Abstract
The second phase of the InterPARES Project built upon the findings of the first phase (1999– 2001) to address the challenge of the permanent preservation of reliable, accurate, and authentic digital records created and maintained in interactive and dynamic systems in the course of all kinds of human activities. This overview describes the goal and methods of the project, and outlines findings and products to provide a framework for the articles published in the Archivaria section entitled, “Reflections on InterPARES.” RÉSUMÉ La deuxième phase du projet InterPARES s’appuie sur les conclusions de la première phase (1999–2001) afin d’aborder le défi de la préservation permanente de documents numériques fiables, exacts et authentiques, créés et maintenus dans des systèmes interactifs et dynamiques au cours de toutes sortes d’activités humaines. Ce survol décrit le but et les méthodes de ce projet, et il dresse les grandes lignes des conclusions et des produits afin de fournir un cadre de travail pour les articles publiés dans Archivaria, dans la section intitulée «Réflexions sur InterPARES».
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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".