Answering populist attacks on psychosocial ideas: why Fromm matters more than Marcuse
Bibliographic record
Abstract
The reactionary American intellectual Christopher Rufo has made German critical theorist Herbert Marcuse the centre of his campaign to purge the American academy of radical ideas and movements. Marcuse’s ideas have significant influence in contemporary psychosocial scholarship, so attacks on his work may have negative consequences for psychosocial scholars. Rufo’s critique of the influence of Marcuse’s ideas is mostly exaggerated but it contains elements of truth. This article will outline ways in which some of Marcuse’s ideas are echoed in elements of the contemporary left/liberal intellectual and political orthodoxy. We revisit the Fromm/Marcuse debate from the 1950s, and offer an analysis of why Rufo might have picked Marcuse for attack when Fromm might well have been a viable target, as Fromm was in the 1980s when he was famously scapegoated by Allan Bloom in The Closing of the American Mind (1987). I then offer an analysis of how Erich Fromm’s alternative psychosocial radicalism can help better defend the psychosocial perspective in mass politics than Marcuse’s framework. Fromm’s framework also offer a theoretical foundation for radical psychosocial studies that can help our field defend itself against the new McCarthyism of Rufo and his allies on the global right who are likely to attack radical psychosocial perspectives in the near future.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".