NOT FOR DISTRIBUTION OR PUBLICATION
Bibliographic record
Abstract
Facilities Guidelines (TS-AFG) has continued to review the facility guidelines, assess their application, evaluate new research, and solicit comments and reviews. This document supplies the first draft revisions to the 2009 standards for archival facilities. Not all chapters have been included here. The chapters included in this review draft are in the early stages of development, have not been formally edited or formatted, and do not include related graphs, charts, text boxes and photographs. They have not been reviewed by the SAA Standards Committee, the SAA Council, or outside groups and individuals. Please note that the chapters are early drafts and some material will be cut or reduced to tables and charts in the final drafting. We are making them available at this time for informal review at the Society of American Archivists meeting being held in Washington, DC in August 2014. We have found that the annual conference is an excellent time to discuss our work on archival facilities with colleagues. The completed working draft will be submitted to the SAA Standards Committee for formal review. The SAA Standards Committee will circulate the working to US and Canadian archival organizations; fellow professionals in NAGARA, CoSA, ICA, ALA, AAM; and architects, archivists, conservators, and construction specialists for comment and input. Comments are encouraged and welcome. If you have comments or suggestions please send them to:
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.860 | 0.879 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".