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. \nDrawing 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".