Beyond Utility: Analyzing Unseen Infrastructures of Necromobility
Bibliographic record
Abstract
The year 2020 was unprecedented on varying accounts but will undoubtedly be remembered by a global pandemic bringing the world to a shuddering halt. As nations scrambled to flatten the outbreak’s curve, the virus tested the capacity of healthcare systems and local morgues, and with the overwhelming number of fatalities came the shocking images of the pandemic’s consequences: the erection of temporary morgues and mass graves. While current measures inherit guidelines by global authorities, the Canadian government has yet to provide a tailored approach that addresses local limitations and resources in times of mass fatalities. \nAs hospitals and morgues operated near capacity at the start of pandemic system, monumental efforts have been made to alleviate pressures on our healthcare system, while deathcare remains in a historically precariousstate. With increasing rates of death, various ad-hoc solutions have emerged to supplement unit capacities and to facilitate social distancing at traditional services. Acknowledging that present research predisposes care of the living, Beyond Utility aims to shed light on the invisible industry that cares for the dead and its crucial role in maintaining public health. By documenting the journey of bodies, from time of death to final disposition, the unseen processes within the deathcare industry captures a system that has been historically overlooked, understaffed, operating near capacity. \nUsing case studies of deathcare crises in Ontario, research addresses inherent spatial constraints within institutions, conflicting interests between stakeholders and legislations, and programmatic limitations from ad-hoc constructions. While the unexpected deaths due to COVID-19 have only further strained deathcare operations, Beyond Utility argues the permanence of ad-hoc constructions, the adaptability of current deathcare practices, and the need for collaborative planning between agencies and disciplines as service demands rise.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".