What Hindu <i>Sati</i> can teach us about the sociocultural and social psychological dynamics of suicide
Bibliographic record
Abstract
Abstract By leveraging the case of Hindu sati, this paper elucidates the ways in which structure and culture condition suicidal behavior by way of social psychological and emotional dynamics. Conventionally, sati falls under Durkheim's discussion of altruistic suicides, or the self‐sacrifice of underindividuated or excessively integrated peoples like widows in traditional societies. In light of the fact that Durkheim's interpretation was based on uneven data, nineteenth century Eurocentric beliefs, and a theoretical framework that can no longer resist modification and elaboration, by reconsidering sati it is possible to sketch a new model that strengthens Durkheim's theory by making it more robust and generalizable. The following model is built on five principles. First, integration and regulation are not distinct causal forces, but overlapping contextual conditions. Second, to better explain the variation in suicidality across time and space, we must also pay attention to culture as it provides the underlying meanings of suicide that can increase the odds a person or class of persons become suicidal or are protected against suicidality. Third, structure still matters, but in many cases, the role power and power‐differentials play must be considered. Fourth, understanding why and how people choose suicide depends on incorporating identity and status processes. Fifth, because the expression of social emotions like shame are patterned by structural and cultural conditions, to understand how suicidality is socioculturally patterned we must further explore the link between identity/status, social emotions, and structure and culture.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".