Speaking for the Dead: Coroners, Institutional Structures, and Risk Management
Bibliographic record
Abstract
Based on interviews and ethnographic fieldwork, this dissertation shows how the Office of the Chief Coroner of Ontario (OCC) – whose object is to speak for the dead to protect the living – is shaped by risk management priorities. It illustrates how the OCC, like many contemporary organizations, has altered its operations and decision making to manage threats to its reputation. The result of these moves has been the privatization of public safety decision making with bereaved families, the general public, and even front line coroners, increasingly excluded from speaking for the dead. This is to say, policy recommendations that shape how life in Ontario is lived tend to be generated in private sessions by OCC managers. While much of this can be attributed to the OCC’s focus on reputational risk management, there are other important factors affecting the privatization of public safety. Drawing on research in the sociology of culture, the dissertation finds that the OCC’s experience of risk management is moderated by other, layered institutional structures. These ‘institutional structures’ are analytic constructs with moral and methodological dimensions that inform the way work in the OCC is carried out. The dissertation demonstrates that the moral priorities and method preferences of doctors, lawyers, managers, families, and modern governments are layered over and under risk management. These layers augment or diminish risk management’s impact on the way death is determined and public safety regimes are developed. In addition to offering a window on death investigators and their work, the dissertation proposes a theoretical toolset for better understanding how contemporary organizations are organized and run.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.053 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".