Shared Relations: Trauma and Kinship in the Afterlife of Death
Bibliographic record
Abstract
Since 2013, we have studied the logic and narratives of an environmental epigenetics research team that studies the correlations between early childhood adversity (ECA), specific biomarkers, and suicide risk. Within this research program, kin of the deceased participate in psychological autopsies, which researchers use to establish to classify the deceased within a typology of suicide with or without abuse. We focus on the words of these family respondents and their reflections on the life and death of their loved ones, and life after that death, to consider the slippery, transgressive, and relational character of trauma and its effects. Studies of the residues of past experiences provide crucial insights into the complex, unpredictable, and unsettled nature of kin relations. These relations are based in entwined biographies of the living and the dead and illustrate the holds that people have on each other and destabilize biomedical models of individualized trajectories of suicide risk. [suicide, psychological autopsies, trauma, care, kinship].
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".