Against Integration or Against Using Evolutionary Theory to Homogenize Psychology?
Bibliographic record
Abstract
This paper comments on an article by Derksen, who argues that the theory of evolution cannot unify the social sciences in general and psychology in particular. Derksen also elevates his ‘against integration’ argument to an in-principle level whereby he rejects integration as a solution to the problem of fragmentation. Where Derksen limits his arguments to evolutionary theory, they are welcome; however, where he elevates his arguments to an in-principle level, they become unmindful of a vast history on fragmentation and integration/unification. Derksen also implicitly defines integration as seamless homogeneity, which is only one definition of integration/unification used in the literature. Finally, his alternatives to integration are found to be lacking in detailed explanation and critical reflection.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.010 | 0.032 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".