A Study on Building the World Literature Data Collection for Digital Humanities
Bibliographic record
Abstract
Data is now utilized in advanced research and scholarship across various fields. Utilizing data in the field of literary research represents a burgeoning area within the digital humanities. World Literature holds a distinct cultural significance in Korea. Traditional cataloging, such as MARC, present challenges in gathering, analyzing, and comprehending various facets. Despite the recent attention given to various data-related topics, techniques, and solutions, the efforts toward data aggregation and dissemination have been disorganized and occasionally inconsistent. This highlights the significance of standards-based data models and metadata. This study has selected 1950~60, part of the published period of the World Literature Collection. The data model has been developed based on BIBFRAME with metadata elements drawn from MODS. The current research has developed a preliminary data model and enhanced metadata for the World Literature Data Collection. The findings of the study have two implications: first, it could contribute to the creation of the data model for organizing book series in a digital library, and second, it could provide valuable insights to literature researchers working in their area.
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 | 0.045 | 0.101 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".