Extending Classification Principles from Information Modeling to Other Disciplines
Bibliographic record
Abstract
Classifying phenomena is a central aspect of cognition. Similarly, specifying classes of interest is a central aspect of information systems analysis and design. We extend principles originally developed to guide classification in information systems to the general problem of organizing scientific knowledge. Two fundamental cognitive principles underlie the choice of classes. First, classes should encapsulate inferences about the properties of their instances. Second, collections of classes should provide economy of storage and processing. This leads to a view of classes as carriers of domain knowledge in the form of inferences about situations, rather than containers for information. In this paper, we show how this view, originally developed in the IT context, can be extended to other disciplines, notably the natural sciences. We explain how the principles of inference and economy can guide the choice of individual classes and collections of classes. Moreover, we present a generalized classification-based information processing system (CIPS) model. We propose that scientific theories can be represented by class structures as defined in our model and demonstrate how this can be done by applying CIPS to analyze an example from the philosophy of science literature dealing with nuclear physics. The example demonstrates two advantages of the CIPS approach: first, it can provide a simpler, more scalable, and more informative account of the phenomena than a competing approach (dynamic frames); second, the resolution of inconsistencies between theory and observation can be framed in terms of changes to classification structures, and the principles can even guide such changes.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".