Well-being at the cost of welfare: Learned helplessness and responsibility in positive psychology and American policy
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".