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Record W2509371626 · doi:10.5206/fpq/2016.1.1

Love Slaves and Wonder Women: Radical Feminism and Social Reform in the Psychology of William Moulton Marston

2016· article· en· W2509371626 on OpenAlexvenueno aff
Matthew J. Brown

Bibliographic record

VenueFeminist Philosophy Quarterly · 2016
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsWonderValue (mathematics)FeminismSociologyPsychoanalysisEpistemologyPsychologySocial psychologyGender studiesPhilosophy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.322
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

Explore more

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