Bibliographic record
Abstract
Purpose This paper aims to respond to the 2005 paper by Hjørland and Nissen Pedersen by suggesting that an exhaustive and universal classification of the phenomena that scholars study, and the methods and theories they apply, is feasible. It seeks to argue that such a classification is critical for interdisciplinary scholarship. Design/methodology/approach The paper presents a literature‐based conceptual analysis, taking Hjørland and Nissen Pedersen as its starting point. Hjørland and Nissen Pedersen had identified several difficulties that would be encountered in developing such a classification; the paper suggests how each of these can be overcome. It also urges a deductive approach as complementary to the inductive approach recommended by Hjørland and Nissen Pedersen. Findings The paper finds that an exhaustive and universal classification of scholarly documents in terms of (at least) the phenomena that scholars study, and the theories and methods they apply, appears to be both possible and desirable. Practical implications The paper suggests how such a project can be begun. In particular it stresses the importance of classifying documents in terms of causal links between phenomena. Originality/value The paper links the information science, interdisciplinary, and study of science literatures, and suggests that the types of classification outlined above would be of great value to scientists/scholars, and that they are possible.
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 | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.027 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".