Personal Papers and MPLP: Strategies and Techniques
Bibliographic record
Abstract
publirent un article exhortant les archivistes rvaluer leurs stratgies de traitement des documents d'archives afin de placer moins d'importance sur les classements et descriptions dtailles et plus sur les efforts minimaux pour rendre les documents accessibles aux chercheurs.En 2008, l'Auburn Avenue Research Library on African American Culture and History, Atlanta, en Georgie, a octroy une bourse du Council of Library and Information Resources afin de traiter les documents personnels d'Andrew J. Young.Les conditions attaches la bourse exigeaient l'adoption des techniques plus de produit, moins de processus de Greene et Meissner.Cet article dcrit l'analyse et les stratgies qui ont men vers la dcision de se servir d'une varit de niveaux de traitement pour classer les documents d'Andrew J. Young, qui couvrent plus de cinquante ans de sa vie publique et prive.Ce projet a permis au personnel des archives de mener des expriences sur une tendue de mthodes pour classer et dcrire la collection, allant d'une description minimale une description la pice.Une fois le projet complt, il fut dcouvert que la meilleure faon de traiter une collection est de ne pas se limiter uniquement la mthode de la description la pice ou celle du plus de produit, moins de processus , mais d'employer les techniques appropries de diverses mthodes afin de crer une stratgie adquate et de longue dure.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".