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Record W4411092707 · doi:10.1177/09526951251336898

Well-being at the cost of welfare: Learned helplessness and responsibility in positive psychology and American policy

2025· article· en· W4411092707 on OpenAlexaff
Ian J. Davidson

Bibliographic record

VenueHistory of the Human Sciences · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsLearned helplessnessWelfarePsychologyPositive psychologyMoral responsibilitySocial psychologyPositive economicsEconomicsPolitical scienceEpistemologyPhilosophyLaw

Abstract

fetched live from OpenAlex

In this article I trace the connected histories of well-being and welfare within contemporary American social science and policy. Focusing on the postwar concept of learned helplessness and its associated notions like hopelessness, industriousness, and laziness, I trace a genealogy that is part of the story of the rise of positive psychology, as well as the disciplinary and cultural shift toward self-control, self-monitoring, and other aspects of a moral self-governance founded on values of responsibility and persistence in pursuing personal well-being. Springing from animal research on rats, dogs, and pigeons, then swiftly toward human subjects such as sufferers of depression, students, and welfare recipients, the trajectory from helplessness and hopelessness toward responsibility and persistence followed and possibly influenced both attitudes toward welfarism and changes in American welfare policies. From Curt Richter’s work on hopeless rats to Martin Seligman and colleagues’ decades-spanning work that culminated in positive psychology (part of the wider happiness studies and science of well-being movements), psychological research is framed within the wider history of American welfare. Coinciding with a growing disciplinary inclination toward individual self-improvement that valued personal responsibility and persistence as key virtues, welfare was continually reformed and dismantled under the same principles.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.383
Teacher spread0.343 · 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.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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