Introduction: Turbulent Circulation: Building a Critical Engagement with Logistics
Bibliographic record
Abstract
Since the mid-20th century, logistics has evolved into a wide-ranging science of circulation involved in planning and managing flows of innumerable kinds. In this introductory essay, we take stock of the ascendancy and proliferation of logistics, proposing a critical engagement with the field. We argue that logistics is not limited to the management of supply chains, military or corporate. Rather, it is better understood as a calculative logic and spatial practice of circulation that is at the fore of the reorganization of capitalism and war. Viewed from this perspective, the rise of logistics has transformed not only the physical movement of materials but also the very rationality by which space is organized. It has remade economic and military space according to a universalizing logic of abstract flow, exacerbating existing patterns of uneven geographical development. Drawing on the articles that make up this themed issue, we propose that a critical approach to logistics is characterized by three core commitments: (1) a rejection of the field’s self-depiction as an apolitical science of management, along with a commitment to highlighting the relations of power and acts of violence that underpin it; (2) an interest in exposing the flaws, irrationalities, and vulnerabilities of logistical regimes; and (3) an orientation toward contestation and struggle within logistical networks.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".