Bibliographic record
Abstract
Faceted navigation is a tool for allowing users to explore a collection of information by applying multiple filters and has become a popular function in commercial search applications, particularly for online retailers and libraries. This study compared and analyzed cases of faceted navigation of national archive of foreign country for the provision of basic data for the adoption of faceted navigation function of South Korea’s national archive. The study has conducted research on the NARA of United States of America, TNA of United Kingdom, and LAC of Canada which currently adopts the faceted navigation function in the national archives. The findings from these analyses were as follows. First, NARA provides filtering on the records as the facet of Data Source, Level of Description, Type of materials, File format, Location and Date Moreover, the provision of parallel selection function on the facets allows the user to narrow down the range of optimum subject through browsing. Second, TNA has the drill-down selection facet selection system, classifies as the facet of records and record creators and the first level, and at the second level, the result list is reclassified so that records are held by and date, record creators are facet of country, creator type and date. Third, LAC provides browsing on the search result as the facet including Type of Material, Found In, Online, Hierarchical Level, Data, and others. South Korea’s national archive portal was only providing search by collection and search within results function. For the provision of more user-friendly interface and convenient search function, further revision on the adoption of faceted navigation function including various facets of records is needed.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".