Circulatory [food] systems: Icelandic bloodmaring, reproductive technologies, and the porosity of female agricultural bodies
Bibliographic record
Abstract
The integration of bloodmaring, the industry of harvesting blood from pregnant horses for the synthesis of equine chorionic gonadotropin (eCG), into Icelandic agricultural forms exposes tenuous interconnections of agrarian life, supply chains, and market interests. Corporealities and biotechnology are intertwined intimately, creating strange bedfellows spanning species, lifeways, nations, and economies within the bucolic landscape of rural Iceland. Governed by biopharmaceutical supply chains, raw resources are obscured from the final medicalized product; a quagmire of interspecies sub-contractors flow through sterilized vials, condensing in the reproductive systems of livestock in North America. Within these networks, female bodies are commoditized and technologized, fused together, and manipulated to exploit their reproductive capacities on an international scale. This paper travels international agricultural networks by tracing the veins on a mare’s neck to the circuits through which blood is reformulated into a biopharmeceutical product biologically recycled in the bodies of other livestock. Engaging feminist theory on new materialisms, science studies, and human and nonhuman animal relationships, this paper provides a multispecies ethnography of bloodmaring by exploring female agricultural bodies ensnared and enlivened via food production supply chains.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".