Repositioning the Base Level of Bibliographic Relationships: or, A Cataloguer, a Post-Modernist and a Chatbot Walk Into a Bar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Designers and maintainers of library catalogues are facing fresh challenges representing bibliographic relationships, due both to changes in cataloguing standards and to a broader information environment that has grown increasingly diverse, sophisticated and complex. This paper presents three different paradigms, drawn from three different fields of study, for representing relationships between bibliographic entities beyond the FRBR/LRM models: superworks, as developed in information studies; adaptation, as developed in literary studies; and artificial intelligence, as developed in computer science. Theories of literary adaptation remain focused on “the work,” as traditionally conceived. The concept of the superwork reminds us that there are some works which serve as ancestors for entire families of works, and that those familial relationships are still useful. Crowd-sourcing projects often make more granular connections, a trend which has escalated significantly with current and emerging artificial intelligence systems. While the artificial intelligence paradigm is proving more pervasive outside conventional library systems, it could lead to a seismic shift in knowledge organization, a shift in which the power both to arrange information and to use it are moving beyond the control of users and intermediaries alike.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it