Life After Homicide: Towards Broader Understandings of Successful Mourning
Bibliographic record
Abstract
Prevalent psychological theories of grief focus letting go of a deceased loved one. This can lead to a narrow understanding of successful mourning and can falsely pathologize some of the positive actions of people who have lost a loved one to murder. Utilizing secondary sources and insights written by homicide survivors, this paper specifically explores active homicide survivorship — that is, people who have become personally, socially and/or politically motivated by both their loss and by an intense and continuing commitment to their lost loved one. The paper argues that we need to examine the topic of mourning not only from an individual and psychological perspective, but as actions that occur within, and are influenced by, broader cultural, social, historical and gendered contexts. By taking an interdisciplinary approach to bereavement that considers broader contexts, a more complex and inclusive understanding of successful mourning can be attained. When such a comprehensive approach is taken, it becomes possible to see the decision to hold on to a lost loved one as life-affirming, rational, and resilient, rather than pathological.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".