Love Slaves and Wonder Women: Radical Feminism and Social Reform in the Psychology of William Moulton Marston
Bibliographic record
Abstract
In contemporary histories of psychology, William Moulton Marston is remembered for helping develop the lie detector test. He is better remembered in the history of popular culture for creating the comic book superhero Wonder Woman. In his time, however, he contributed to psychological research in deception, basic emotions, abnormal psychology, sexuality, and consciousness. He was also a radical feminist with connections to women's rights movements. Marston's work is an instructive case for philosophers of science on the relation between science and values. Although Marston's case provides further evidence of the role that feminist values can play in scientific work, it also poses challenges to philosophical accounts of value-laden science. Marston's work exemplifies standard views about feminist value-laden research in that his feminist values help him both to criticize the research of others and create novel psychological concepts and research techniques. His scientific work includes an account of the nature of psycho-emotional health that leads to normative conclusions for individual values and conduct and for society and culture, a direction of influence that is relatively under-theorized in the literature. To understand and evaluate Marston's work requires an approach that treats science and values as mutually influencing; it also requires that we understand the relationship between science advising and political advocacy in value-laden science.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.049 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".