Bibliographic record
Abstract
The field of knowledge organization, and cataloguing in particular, has increasingly become concerned with bibliographic relationships. Tillett (2001) developed a taxonomy of bibliographic relationships that is largely shared by Functional Requirements for Bibliographic Records (FRBR), with the exception of the “shared characteristic” relationship including such features as shared creator or subject headings. This paper will offer another possible shared characteristic: “memes.” Memes are units of cultural inheritance and include literary tropes, character archetypes, and genre conceits, and can link otherwise unconnected works. Le domaine de l'organisation des connaissances, et du catalogage en particulier, se préoccupe de plus en plus des relations bibliographiques. Tillett (2001) a développé une taxonomie des relations bibliographiques qui est largement partagée par les Functional Requirements for Bibliographic Records (FRBR), à l'exception de la relation «caractéristique partagée» incluant des caractéristiques telles que le partage de créateur ou de vedettes-matière. Cet article proposera une autre caractéristique commune possible: les mèmes. Les mèmes sont des unités d'héritage culturel qui comprennent des tropes et des archétypes de personnages et de genre, et qui peuvent lier des œuvres autrement non liées.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".