Bibliographic record
Abstract
The Z39.50 standard has been in productive use for several years now. However, due to the way the standard has been interpreted by some Integrated Library System (ILS) vendors, the results of using the standard have often been somewhat varied at best and, at worst, non functional. The lack of consistency in the use of Z39.50 has lead to the standard being held in rather lower regard than it deserves in some quarters. It is a matter that can be debated whether the inconsistency in the implementation of the standard is the fault of the implementers ignoring aspects of the standard to make their task simpler or simply a lack of precision in the standard itself. To circumvent the debate, the Z39.50 Implementers Group (ZIG) has defined profiles for the use of the standard that are clearly defined and can be measured. This paper takes a look back at why the Z39.50 standard was invented in the first place and defines briefly how Z39.50 works. The paper then considers why implementations of the standard appear to fail and how those failures manifest themselves. With this information in hand, it is possible to make sense of the answer to the question, how does the Bath profile make Z39.50 work?
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 | 0.043 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".