Theory and Metatheory in the Study of Dual Processing
Bibliographic record
Abstract
In this article, we respond to the four comments on our target article. Some of the commentators suggest that we have formulated our proposals in a way that renders our account of dual-process theory untestable and less interesting than the broad theory that has been critiqued in recent literature. Our response is that there is a confusion of levels. Falsifiable predictions occur not at the level of paradigm or metatheory-where this debate is taking place-but rather in the instantiation of such a broad framework in task level models. Our proposal that many dual-processing characteristics are only correlated features does not weaken the testability of task-level dual-processing accounts. We also respond to arguments that types of processing are not qualitatively distinct and discuss specific evidence disputed by the commentators. Finally, we welcome the constructive comments of one commentator who provides strong arguments for the reality of the dual-process distinction.
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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 | 0.037 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.010 | 0.025 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".