Computer and Information Science, Vol. 1, No. 2, May 2008, all in one file
Bibliographic record
Abstract
Metadata has the proven ability to provide information necessary for successful long-term curation of digital objects. However, without curation metadata itself may deteriorate in terms of its quality and integrity over time. Therefore, a digital curation process needs to incorporate the curation of metadata along with that of data in order to ensure the accurate description of data over time. Unfortunately, no comprehensive method for effective curation of metadata for long periods of time is known to exist at present. Even the Reference Model for Open Archival Information System (OAIS), despite being the most comprehensive and widely adopted framework for long-term data preservation, fails to address the requirements of long-term metadata curation in a comprehensive and unambiguous manner. This paper presents an approach to efficiently curating digital metadata over the long-term that is achieved through articulating the metadata curation related ambiguities of the OAIS Reference Model. The approach essentially involves the use of a "Metadata Curation Model", which is a specialised edition of the "Data Management" module of the OAIS Reference Model, dedicated to the purpose of long-term metadata curation.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.031 |
| 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".