Using Cognitive Principles to Guide Classification in Information Systems Modeling1
Bibliographic record
Abstract
Organizing phenomena into classes is a pervasive human activity. The ability to classify phenomena encountered in daily life in useful ways is essential to human survival and adaptation. Not surprisingly, then, classification-oriented activities are widespread in the information systems field. Classes or entity types play a central role in conceptual modeling for information systems requirements analysis, as well as in the design of databases and object-oriented software. Furthermore, classification is the primary task in applications such as data mining and the development of domain ontologies to support information sharing in semantic web applications. However, despite the pervasiveness of classification, little research has proposed well-grounded when modeling a domain or designing information systems artifacts. In this paper, we adopt the cognitive notions of inference and economy to derive a set of principles to guide effective and efficient classification. We present a model for characterizing what may be considered useful classes in a given context based on the inferences that can be drawn from membership in a class. This foundation is then used to suggest practical design rules for evaluating and refining potential classes. We illustrate the use of the rules by showing that applying them to a previously published example yields meaningful changes. We then present an evaluation by a panel of experts who compared the published and revised models. The evaluation shows that following the rules leads to semantically clearer models that are preferred by experts. The paper concludes by outlining possible future research directions.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| 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".