How Do Different Case Conclusions Impact on Survivors of Homicide? Developing and Applying a Conceptual Framework to Organize Current Empirical Knowledge
Bibliographic record
Abstract
Supporting families and friends of homicide victims (‘survivors’) requires understanding how homicide impacts on survivors. Although recent work has examined how the loss of a loved one and events following a homicide—such as media coverage, the criminal justice processes and the perpetrator’s sentence—affects survivors, there has been little consideration of how final ‘case conclusions’ (other than the perpetrator’s sentence)—such as homicide–suicide, cold-case homicide or perpetrator declared permanently unfit for trial or acquitted of murder or manslaughter—impacts on survivors. This article examines existing literature about how different final case conclusions, other than the perpetrator’s sentence, impact on survivors. A novel conceptual framework—the ‘Homicide Case Pathway’—is presented to organize these efforts. There are shared and diverse effects of final case conclusions on survivors, centred on five key themes—emotions and feelings, denial of justice, lack of closure, belief in the system and hope. There is a clear need to conduct further research into the effect of final case conclusions on survivors, in order to better understand survivors’ experiences, and subsequently identify and implement suitably tailored victim support strategies for survivors.
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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 | 0.044 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".