Emancipating instances from the tyranny of classes in information modeling
Bibliographic record
Abstract
Database design commonly assumes, explicitly or implicitly, that instances must belong to classes. This can be termed the assumption of inherent classification . We argue that the extent and complexity of problems in schema integration, schema evolution, and interoperability are, to a large degree, consequences of inherent classification. Furthermore, we make the case that the assumption of inherent classification violates philosophical and cognitive guidelines on classification and is, therefore, inappropriate in view of the role of data modeling in representing knowledge about application domains. As an alternative, we propose a layered approach to modeling in which information about instances is separated from any particular classification. Two data modeling layers are proposed: (1) an instance model consisting of an instance base (i.e., information about instances and properties) and operations to populate, use, and maintain it; and (2) a class model consisting of a class base (i.e., information about classes defined in terms of properties) and operations to populate, use, and maintain it. The two-layered model provides class independence . This is analogous to the arguments of data independence offered by the relational model in comparison to hierarchical and network models. We show that a two-layered approach yields several advantages. In particular, schema integration is shown to be partially an artifact of inherent classification that can be greatly simplified in designing a database based on a layered model; schema evolution is supported without the complexity of operations currently required by class-based models; and the difficulties associated with interoperability among heterogeneous databases are reduced because there is no need to agree on the semantics of classes among independent databases. We conclude by considering the adequacy of a two-layered approach, outlining possible implementation strategies, and drawing attention to some practical considerations.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.014 |
| 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".