(Im)mobilizing Technology: Slow Science, Food Safety, and Borders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. Keywords: Immobilityslow sciencefood safetyborders Acknowledgments Research reported in this article was supported in part by a Social Science and Humanities Research Council Standard Research Grant. The Ottawa office of the Taiwan Economic and Cultural Office helped to arrange interviews in Taiwan. The article was based on a paper presented in a panel at the 2009 American Anthropological Association organized by Noel Salazar, whom we owe thanks for encouraging us to develop our work in this direction. Numerous individuals have contributed to, but are not responsible for, ideas in this article through comments on that presentation or earlier drafts, including John Clarke, Paul Hansen, Josiah Heyman, Pal Nyiri, and Mimi Sheller. Thoughtful comments from two anonymous reviewers and the journal editors contributed significantly to the improvement of this essay. In particular, we are grateful to Sharryn Kasmir for providing us with the concise formulation of "holism-without-borders." Ann Rainville provided helpful editorial and research assistance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 | 0.005 |
| 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 it