Future of the OPAC: Integrating the OPAC with Emerging Discovery Tools
Bibliographic record
Abstract
This presentation will explore how the library catalogue, a tool with a long and distinguished history, is changing with the advent of the Internet. We, as library practitioners, know the power of the catalog to find information sources in library collections. But our users, who have become used to having some results regardless of what search terms they enter into Web search engines, are becoming increasingly frustrated with the current OPAC technologies. What are some of limitations of online library catalogues and in what ways are members of the information community and library automation vendors discovering ways of making local resources more available to our users? Information will be presented about recent studies of user behaviour as well as some commercial solutions to the problem of user/catalog interface.
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 | 0.039 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.026 | 0.057 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".