Towards a psychopolitical approach to nostalgia
Bibliographic record
Abstract
Whether through divisive injunctions to “Make America Great Again” (MAGA), or the impassioned calls to “Take Back Control” that punctuated the Brexit campaign, nostalgia is a salient force in contemporary life. Yet, in psychology, nostalgia is increasingly celebrated as a predominantly positive, universally shared experience. We argue that this disconnect reflects a broader neoliberal pulse running through psychological research, one that frames certain affective phenomena as resources for individual optimization. In response, the current article argues for a psychopolitical understanding of nostalgia—one that foregrounds the entanglement of emotion, memory, and subjectivity within historical and institutional power relations. Through a historiographical overview and critical analysis of the psychological literature, we demonstrate how hegemonic forms of psychological science depoliticize nostalgia, and we call instead for epistemologically generative and ethically responsive theories and methods attuned to its ambivalent and culturally mediated character. We conclude by introducing two conceptual paths for future research, vicarious and speculative orientations to nostalgia, together with some methodological examples drawn from the interdisciplinary scholarship.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.100 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| 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".