Bibliographic record
Abstract
Two fundamental changes are taking place in the electronic serials environment that will transform both the publishing and the library communities. First, the nature of the “serial” is changing, as online reference works are increasingly purchased on subscription. We have traditionally defined published information according to the format in which it was published in print. But encyclopedias, monographs, and journals all become content on the Web. Publishers will package reference works and jour-nals-and even monographs-together as subscription-based services. The challenge for librarians is to identify the different components of the service, locate the peer-reviewed research paper, and provide navigation aids to the user. Second, the evidence from usage as widely spread as the USA, Canada, and Australia indicates that access to packages of online serials both broadens usage and increases volume dramatically. This challenges traditional techniques of collection management and presents opportunities to redefine the business models for acquiring e-serials. This paper will explore these changes and present evidence that these trends will change the way in which publishers and librarians do business together. At last we can cast off the print legacy that affects our thinking about the creation and delivery of information online.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.014 |
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".