(Im)mobilizing Technology: Slow Science, Food Safety, and Borders
Bibliographic record
Abstract
Immobilization is generally thought to result from power and poverty acting against the acceleration produced by science and technology. In this article we explore neglected countervailing trends, such as quarantines, health inspections, and import bans, where science has the effect of restricting mobility, which we refer to as “slow science.” As well as increasing mobility, science can be mobilized for political projects of restricting movement, but this possibility is neglected because of cultural assumptions fundamental to modernity. Both science and technology can be enrolled for projects of slowing mobility as well as increasing mobility. Drawing on actor-network theory, we examine the enrolment of science and technology into restricting movement in various ways. These issues are explored first through an overview of the neglected genealogy of the ways in which science and technology have slowed movement, particularly across national borders, and second through a short case study of how food safety concerns affect the movement of beef across borders. The case study discusses how “slow science” diagnoses threats posed by mobility and develops technologies to immobilize certain entities. These entities have almost always been biological organisms (including humans) or their products due to the self-reproducing qualities of invasive species, bacteria, or viruses. Uniquely, WTO rules about food require that restrictions be based on sound science, resulting in trade disputes focused on scientific interpretations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".