Theorizing in psychology: From the critique of a <i>hyper-science</i> to conceptualizing subjectivity
Bibliographic record
Abstract
It is emphasized that theoretical psychology is based on the practice of theorizing. After presenting and challenging some ideas about theories, three tasks for theoretical psychologists are discussed: theorizing as critique refers to describing psychology as a hyper-science that inflates its methodological and technical activities in order to conceal its lack of a natural–scientific foundation. Theorizing as reconstruction is an activity that seeks to make sense of history, culture, society, power, money, and politics in the development of the discipline and subjectivity. This task also traces the elements that have led to psychology’s difficulties. Theorizing as creation means developing theories that are able to make sense of psychological knowledge. It is suggested that a theory of subjectivity is needed that draws on not only the psychological sciences but also the psychological humanities. Finally, it is argued that theorizing remains a duty of psychological work.
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 | 0.034 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.168 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".