Informational equivalence, computational equivalence, and the evaluation of conceptual modelling
Bibliographic record
Abstract
A number of researchers have proposed guidelines for the design of empirical research to evaluate the strengths and weaknesses of alternative conceptual modelling grammars and scripts. Some guidelines have been founded on the argument that a comparison of alternative conceptual modelling scripts generated via the same or different conceptual modelling grammars requires the scripts to be “informationally equivalent.” In other words, the scripts should provide alternative representations of the same semantics in a domain. Otherwise, differences in a user’s ability to comprehend the scripts are confounded by differences in the semantics represented by the scripts. We present a contrary view. When empirical comparisons of conceptual modelling grammars and scripts are motivated by an ontological benchmark, we argue that the goal is often to show that informational equivalence does not exist in the script and that users’ understanding of the scripts is thereby undermined.
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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 | 0.138 | 0.516 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.015 | 0.042 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".