Chains and networks, territories and scales: towards a relational framework for analysing the global economy
Bibliographic record
Abstract
A vast and continually expanding literature on economic globalization continues to generate a miasma of conflicting viewpoints and alternative discourses. This article argues that any understanding of the global economy must be sensitive to four considerations: (a) conceptual categories and labels carry with them the discursive power to shape material processes; (b) multiple scales of analysis must be incorporated in recognition of the contemporary ‘relativization of scale’; (c) no single institutional or organizational locus of analysis should be privileged; and (d) extrapolations from specific case studies and instances must be treated with caution, but this should not preclude the option of discussing the global economy, and power relations within it, as a structural whole. This paper advocates a network methodology as a potential framework to incorporate these concerns. Such a methodology requires us to identify actors in networks, their ongoing relations and the structural outcomes of these relations. Networks thus become the foundational unit of analysis for our understanding of the global economy, rather than individuals, firms or nation states. In presenting this argument we critically examine two examples of network methodology that have been used to provide frameworks for analysing the global economy: global commodity chains and actor‐network theory. We suggest that while they fall short of fulfilling the promise of a network methodology in some respects, they do provide indications of the utility of such a methodology as a basis for understanding the global economy.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".