Bibliographic record
Abstract
This article develops the concept ‘economies of affect’ to argue for increased anthropological attention to the roles of affect in facilitating economic transformations. The article draws on evidence from two ethnographic field projects, one in Mexico and the other in Indonesia, to show how affect was mobilized to create subjects commensurable with neoliberal norms. We show how embracing and crying and discourses about love and grief were conjoined to transformations that entailed the cessation of state guarantees and the introduction of market norms. In posing affect and its articulation with questions of economic change as an object of anthropological inquiry, the article argues for the utility of a notion of affect in contrast to other approaches that have stressed emotion. We argue that affect is useful because it is inherently reflexive and intersubjective. Affect refers to relations practised between individuals, in contrast to emotion, which still bears the spectre of a psychological individualism. Résumé Cet article développe le concept des «économies de l'affect» pour attirer l'attention des anthropologues sur le rôle de l'affect dans la facilitation des transformations économiques. Il s'appuie sur les résultats de deux projets de terrain ethnographiques, l'un au Mexique et l'autre en Indonésie, pour montrer comment l'affect a été mobilisé pour créer des sujets pouvant être appréhendés selon les normes néolibérales. Les auteurs montrent comment l'étreinte, les pleurs et les discours sur l'amour et le chagrin ont été associés à des transformations impliquant la cessation de prestations de l'État et l'application des lois du marché. En faisant de l'affect et de son articulation avec le changement économique un objet d'étude anthropologique, l'article affirme l'utilité d'une notion d'affect se démarquant d'autres approches qui mettent l'accent sur l'émotion. Les auteurs affirment que l'affect est utile parce qu'il est, par nature, réflexif et intersubjectif. L'affect renvoie aux relations pratiquées entre les individus, à la différence de l'émotion, toujours marquée par le spectre d'un individualisme psychologique.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".