Bibliographic record
Abstract
As e‐books become an increasingly large part of our collection, the NCSU Libraries acquisition and discovery department created an e‐book reconciliation database to ensure that all of our purchased e‐book package content is available in the ILS and throughout the Libraries discovery layers and to create definitive title lists that associate and articulate e‐book titles with package purchases. This tool compares vendor title lists against ILS metadata in order to identify missing titles and generate reports. The paper will discuss what prompted the development of the database; present the e‐book data flow in NCSU Libraries and e‐book reconciliation workflows designed based on the data flow; report on our approach on how to collect e‐book title lists, normalize metadata and identify matching point for the reconciliation; present our findings by analyzing the data; and discuss the most common e‐books issues found in the reconciliation process, the causes and solutions for these issues.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".