Bibliographic record
Abstract
As recently as 1995, sweet blood did not resonate broadly as an urgent transnational concern. This thesis chronicles how diabetes mellitus, sweet blood, became recognized as a social problem besetting Canada, among many other countries. This ethnographic study brings anthropological theories---developed for the most part to analyze the lives of "non-Western" peoples---to bear on "Western" philosophy, science, medicine, mass media, governments, and commerce. Throughout, this thesis challenges received wisdom about disease, technologies, kinship, commodification, embodiment, and personhood. This thesis argues that a statistical concept, the population, is the linchpin of both politics and economics in large-scale societies. Statistically-fashioned populations, combined with the conviction that the future can be partially controlled, undergird the very definition of diabetes as a disease. In turn, biomedical knowledge about diabetes grounds the understanding of sweet blood as a social problem in need of better management. The political economy of sweet blood shows that, under "Western" eyes, persons can remain intact while their bodies---down to their very cells---divide and multiply, both literally and figuratively. As members of statistically-fashioned populations, human beings have a patent existence and many "statistical doubles." These statistical doppelgangers help shape feelings, actions, identities, and even the length of human lives. They permit countless strangers and "lower" nonhuman beings---among them, mice, flies, and bacteria---to count as kin. Through the generation and use of statistics, people and their body parts undergo valuation and commodification, but are neither bought nor sold. The use of statistics to commodify human beings and body parts, this thesis finds, inevitably anchors biomedical practice, biomedical research, health policies, and the marketing of pharmaceuticals and all other things known to affect health.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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; both teacher heads agree on what is shown here.
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".