Encyclopedia of archival science
Bibliographic record
Abstract
Here is the first-ever comprehensive guide to archival concepts, principles, and practices. Encyclopedia of Archival Science features 154 entries, which address every aspect of archival professional knowledge. These entries range from traditional ideas (like appraisal and provenance) to today's challenges (digitization and digital preservation). They present the thoughts of leading luminaries like Ernst Posner, Margaret Cross-Norton, and Philip Brooks as well as those of contemporary authors and rising scholars. Historical and ethical components of practice are infused throughout the work. Edited by Luciana Duranti from the University of British Columbia and Patricia C. Franks from San Jose State University, this landmark work was overseen by an editorial board comprised of leading archivists and archival educators from every continent: Adrian Cunningham (Queensland State Archives, Australia), Fiorella Foscarini (University of Toronto and University of Amsterdam), Pat Galloway (University of Texas at Austin), Shadrack Katuu (International Atomic Energy Agency), Giovanni Michetti (University of Rome La Sapienza), Ken Thibodeau (National Archives and Records Administration, US), and Geoffrey Yeo (University College London, UK).
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.210 | 0.140 |
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".