Using Category Partition to Detect Metamorphic Relations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Category Partition (CP) functional testing method has proven to be useful in various contexts. It begins by identifying parameters and environment conditions on the basis of the function's behaviour. The characteristics/categories of these parameters/environment conditions are identified and partitioned into choices. The choices of a category are mutually exclusive and can be based on input partitioning and boundary value analysis. Thereafter, the choices are combined on the basis of a selection criterion to form test frames. Once input values satisfying the conditions of a test frame's choices are identified, one is equipped with a test case. This paper suggests and demonstrates that those test frames, once equipped with characterizations of output values, i.e., with categories and choices for outputs, can be considered Metamorphic Relations to be used in Metamorphic Testing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it