Library Trends 54 (1) Summer 2005: Digital Preservation: Finding Balance
Bibliographic record
Abstract
The preservation of digital resources is about finding ways to maintain our digital heritage, whether it exists in the form of e-journals, database records, Web sites, emails, digital images, audio-visual materials, interactive programs, or any other kind of binary data. Libraries frequently engage with a wide variety of these resources and understand how quickly and easily we lose use of them when computers change or links break. \n \nOver the past ten years, many different solutions have been proposed, and as many opinions have been voiced about whether these solutions will work. While it is often perceived that these opinions contradict or argue with each other, the real answer is in finding the balance of what works in a particular situation. With this issue of Library Trends we may start to see how each solution has its own benefit in a particular context. (adapted from the introduction)
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.065 | 0.015 |
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".