Circulating tears and managing hearts: Governing through affect in an Indonesian steel factory
Bibliographic record
Abstract
This article argues that moderate Islamic spiritual training programs in contemporary Indonesia entail ‘governing through affect’. This formulation captures the embodied dispositions and ritual forms through which affect is mobilized to serve as a modality of government. Based on over two years of ethnographic research in Indonesia, most of which took place at Krakatau Steel in western Java, I examine the affective incitements that took place in ritual settings dedicated toward corporate productivity and self-improvement. The article argues that this process of subjectification took place in three stages. First, what participants referred to as ‘opening the heart’, which involved making participants receptive to the message of work as worship through recourse to affective enactments in Islamic history and discourse. Second, the circulation of tears, which refers to deep collective weeping which simultaneously represented and physically enacted the adoption of a new subjectivity. Finally, ‘managing the heart’ — the ultimate goal of spiritual training — was a form of self-management in which one exercised ‘built-in control’ and acted in ways that were deemed simultaneously conducive to corporate competitiveness and other-worldly salvation. I conclude that affect constitutes the virtuality of ritual insofar as it is the medium through which spiritual reform enters into the vital processes of human life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".