Bibliographic record
Abstract
Abstract Interdisciplinary communication, and thus the rate of progress in scholarly understanding, would be greatly enhanced if scholars had access to a universal classification of documents or ideas not grounded in particular disciplines or cultures. Such a classification is feasible if complex concepts can be understood as some combination of more basic concepts. There appear to be five main types of concept theory in the philosophical literature. Each provides some support for the idea of breaking complex into basic concepts that can be understood across disciplines or cultures, but each has detractors. None of these criticisms represents a substantive obstacle to breaking complex concepts into basic concepts within information science. Can we take the subject entries in existing universal but discipline‐based classifications, and break these into a set of more basic concepts that can be applied across disciplinary classes? The author performs this sort of analysis for Dewey classes 300 to 339.9. This analysis will serve to identify the sort of ‘basic concepts’ that would lie at the heart of a truly universal classification. There are two key types of basic concept: the things we study (individuals, rocks, trees), and the relationships among these (talking, moving, paying).
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".