Bibliographic record
Abstract
This paper will outline some of the key aspects of the FRBR family of conceptual models that support resource discovery especially for persons who are blind, visually impaired, or otherwise print disabled. The FRBR family of models have had a significant influence on the ways in which communities around the globe perceive and understand the bibliographic universe. This paper will focus on two areas where the conceptual models have had an important impact: bibliographic information as data and the precise delineation between content and carrier. The paper focuses on these two areas because they are of particular interest for a user with a print disability who approaches the task of discovering an appropriate resource. FRBR modeling, as expressed in the original models or in the new consolidated model, FRBR-LRM, offers a roadmap for structuring metadata in ways that allow more options for resource discovery in an increasingly global context.
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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.011 | 0.038 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".