Bibliographic record
Abstract
The PREMIS Implementation Fair Series is for anyone with some knowledge of PREMIS who might be planning or is already involved in a PREMIS implementation. The first fair took place on October 7 2009 in San Francisco, California, USA, immediately following iPres 2009. This record contains the original annoucement and agenda as well as all presentations and a brief summary report. In detail, the files contained within this dataset are: Agenda and Invitation 00_Agenda_PREMIS_Implementation_Fair_2009.pdf Summary of the event 00_Summary_PREMISImplementationFair_2009.pdf Status of PREMIS (Brian Lavoie, OCLC) 01_stateofPREMIS.ppt Implementation in METS (Rebecca Guenther, LC) 02_premis-mets-pif.ppt PREMIS in METS Toolkit (Francesco Lazzarino, FCLA) 03_PIM_presentation.pdf Hub and Spoke Framework Tool Suite (Bill Ingram, UIUC) 04_hands_premis_fair.pdf Statistics New Zealand PREMIS tool (Euan Cochrane) 05_PREMISfair-Euan_Cochrane.ppt Rosetta (Yair Brama, Ex Libris) 06_PREMIS_Implementation_Fair-brama.ppt DAITSS (Priscilla Caplan, FCLA) 07_PREMIS_and_DAITSS.ppt Discussion of potential changes to the PREMIS data model using Events in Portico as an exmaple (Priscilla Caplan and Evan Owens) 08_PREMIS_Data_Model.ppt 09_Portico_PREMIS_Workshop.ppt Case Studies: PREMIS Rights implementation at University of California San Diego (Bradley Westbrook, UCSD) 10_PREMISimpWorkshop09-UCSD.ppt Implementation in Italy (Angela Di Iorio, Fondazione Rinascimento Digitale) 11_AngelaDiIorio_PREMIS_Implementation_Fair.ppt 12_ARTAT_PREMIS_SEMANTIC_UNITS_ROADMAP.xls PREMIS for geospatial data (Nancy Hoebelheinrich) 13_PREMIS_ImplementFairnjh.ppt Towards Interoperable Preservation Repositories (TIPR) project (Priscilla Caplan, FCLA) 14_PREMIS_in_TIPR.ppt National Library of Finland digitized monographs incl. PREMIS in Mets example (Karo Salminen and Jukka Kervinen, National Library of Finland) 15_premis_implementation_at_NLF.ppt 16_mono_v0_53_modified_for_dt.xml PREMIS Implementation at the British Library (Markus Enders) 17_Premis_at_the_British_Library.ppt Conformance: New PREMIS conformance statement (Brian Lovoie, OCLC) 18_premisConformance.ppt Controlled vocabularies, Library of Congress' Authorities & Vocabularies service (Rebecca Guenther, LC) 19_controlled-vocabs-pif.ppt
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 | 0.030 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.562 | 0.388 |
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".