Not every part of a tree is a tree: A reply to Matta and Frank (2025).
Bibliographic record
Abstract
Our recent article on congruence research (Yao & Ma, 2023) advocated the need to adopt a holistic approach to studying congruence effects and to developing stronger congruence theories. Matta and Frank (2025) offered an insightful commentary on our article, highlighting theoretical and empirical/inferential concerns. These concerns include (a) whether the exact correspondence effect is the theoretical goal and (b) when researchers should consider applied conditions or reported conditions in congruence research. While Matta and Frank acknowledged the value of the holistic perspective, they recommended testing one's hypothesized form of congruence as the goal of future congruence research. We thank Matta and Frank for bringing up these issues. These issues have gained increased relevance and urgency especially after Yao and Ma (2023) identified several common issues in published congruence studies and offered suggestions for improvements. This reply is intended to clarify and extend our arguments on the holistic perspective, illustrating how this perspective can help address the concerns they raised and further advance congruence research. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".