Bibliographic record
Abstract
Metadata practices in libraries have been shifting towards a graph-centric data model for a number of years due to the influence of the Semantic Web on metadata standards as well as the ongoing engagement of libraries with linked data. This trend is likely to be sustained by the growth of the knowledge graph domain, which is animated by the interests of large technology companies and which represents a continuation of earlier programmes such as expert systems and the Semantic Web. Given the role of Semantic Web ontologies in knowledge graph development and the relevance of philosophical questions of ontology to cataloguing theory, metadata practitioners require theoretical frameworks suitable for conceptualizing the knowledge graph data model’s mixture of data and ontology. To that end, this paper considers the mathematical ontology of philosopher Alain Badiou, which employs set theory to schematize a theory of the multiple. It outlines how Badiou’s ontology is compatible with the graph data model and what it offers to metadata practitioners seeking to critically engage the knowledge graph paradigm.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.010 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".