Bibliographic record
Abstract
Over the past few decades, a large literature has emerged on the question of how one might unify all or most of psychology under a single, coherent, rigorous framework, in a manner similar to that which unified physics under Newton's Laws, or biology under Darwin's theory of natural selection. It is argued here that this is a highly unlikely scenario in psychology given the contingent and opportunistic character of the processes that brought its original topics together into a new discipline, and the nearly continuous institutional, social, and even political negotiating and horse-trading that has determined psychology's “boundaries” in the 14 decades since. Psychology, as the field currently stands, does not have the intellectual coherence to be brought together by any set of principles that would enable its phenomena to be captured and explained as rigorous products of those principles. If there is a kind of unification in psychology's future, it is more likely to be one that, paradoxically, sees it broken up into a number of large “super-subdisciplines,” each of which exhibits more internal coherence than does the current sprawling and heterogeneous whole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".