Bibliographic record
Abstract
Preserving Digital Materials Kapena Shim, Master’s Candidate, Library & Information Science, UH Mānoa What are digital materials? Digital copies of digitized materials; Born digital files; Digital records of Institutions; Examples include text-based files, image-based files, sound-based files; web-page based files. What are the preservation issues? Technology Obsolescence: Machine dependency to be read; Rapid changes in technology outdates pre-existing machines & software resulting in unreadable files. Physical Deterioration: Fragility of media; Damages & corrupts easily from exposure to heat, humidity, airborne containments, faulty reading and writing devices. Legal Issues: intellectual property rights (IPR); Refreshing, emulating, migrating can infringe IPR unless permitted by copyright holder or law. Preservation Precautions: Stable, safe storage; Controlled environment; Regular refreshment cycles onto new media; Make preservation copies; Handle properly; Transfer to standard storage media; Use standard files and media formats; Detailed metadata documentation. Preservation Strategies: Migration; Emulation; Technology Preservation; Convert to Analog Format.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.197 | 0.082 |
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".