Bibliographic record
Abstract
Over the past 20 years, plasma has become a medical treatment characterized as "liquid gold" to signal its lifesaving potential. Through a manufacturing process termed fractionation, plasma, collected through blood donation, is turned into Plasma Derived Medical Products (PDMPs). The World Health Organization (WHO) has underlined the importance of PDMPs for global health care, including a number of PDMPs on the WHO Model List of Essential Medicines. The process of collecting plasma from a donor, manufacturing plasma derived treatments, and distributing those treatments globally requires the coordination of multiple social actors operating in different social, political and economic contexts, but has received little attention in scholarly literature on public policy or the social sciences. This paper will introduce a set of analytic questions and concepts that can direct a sociology of plasma products. We build on the behavioral turn in the policy sciences to identify relevant policy questions emerging from this field and offer the analytic tools necessary to investigate how different social actors in this space make meaning of plasma. To do this, we will draw on key concepts in the sociology of health and illness.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".