From Psychological Science to the Psychological Humanities: Building a General Theory of Subjectivity
Bibliographic record
Abstract
The development of psychology as a science and the struggle for scientific recognition has disrupted the need to interrogate the discipline and the profession from the perspective of the humanities, the arts, and the concept-driven social sciences. This article suggests that some of the humanities contribute significantly to an understanding of human subjectivity, arguably a core topic within psychology. The article outlines the relevance of the psychological humanities by reclaiming subjectivity as a core topic for general psychology that is grounded in theoretical reconstruction, integration, and advancement. The argument relies on a variety of disciplines to achieve a deeper understanding of subjectivity: Philosophy provides conceptual clarifications and guidelines for integrating research on subjectivity; history reconstructs the movement of subjectivity and its subdivisions; political and social theories debate the process of subjectification; indigenous, cultural, and postcolonial studies show that Western theories of subjectivity cannot be applied habitually to contexts outside of the center; the arts corroborate the idea that subjective imagination is core to the aesthetic project; and science and technology studies point to recent developments in genetic science and information technology, advances that necessitate the consideration of significant changes in subjectivity. The implications of the psychological humanities as an important, justifiable tradition in psychology and for a general theory of subjectivity are discussed.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.076 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| 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".