Name Authority Challenges for Indexing and Abstracting Databases
Bibliographic record
Abstract
Objective - This analysis explores alternative methods for managing author name changes in Indexing and Abstracting (I&A) databases. A searcher may retrieve incomplete or inaccurate results when the database provides no or faulty assistance in linking author name variations. Methods -The article includes an analysis of current name authority practices in I&A databases and of selected research into name disambiguation models applied to authorship of articles. Results - Several potential solutions are in production or in development. MathSciNet has developed an authority file. The method is largely machine-based but it involves time-consuming manual intervention that might not scale up to larger or multidisciplinary databases. The use of standard numbers for authors has been proposed. Solutions in practice include author-managed registration records and linking among several authority files. Information science and computer science researchers are developing models to automate processes for name disambiguation, shifting the focus from authority control to access control. Successful models use metadata beyond the author name alone, such as co-authors, author affiliation, journal name, or keywords. Social networks may provide additional data to support disambiguation models. Conclusion - The traditional objective of name authority files is to determine precisely when name variations belong to the same individual. Manually-maintained authority files have served library catalogues reasonably well, but the burden of upkeep has made them ill-suited to managing the volume of items and authors in all but the smallest I&A databases. To meet the access needs of the 21st Century, both catalogues and I&A databases may need to implement options that present a high degree of probability that items have been authored by the same individual, rather than options that provide high precision with the expense of manual maintenance. Striving for name disambiguation rather than name authority control may become an attractive option for catalogues, I&A databases, and digital library collections.
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 | 0.063 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.029 | 0.053 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".