World-Systems Analysis after Thirty Years: Should it Rest in Peace?
Bibliographic record
Abstract
World-systems analysis has had a major impact on the social sciences over the past three decades. Although originally developed within sociology, its influence has not only been extensive in that field, but has spread to such fields as anthropology and political science as well. This article attempts a new critical assessment of the world-systems paradigm. Its major accomplishments are seen as sixfold: its insistence on understanding the modern world historically, its employment of modes of historical analysis that encompass very long periods of time ( la longue durée), its highly interdisciplinary nature, its rigorous materialism, a conception of capitalism that is broader and more useful than the traditional Marxian conception, and its situation of the current phase of globalization in its proper historical context. On the negative side, I identify five major problems: its tendency toward teleology and reification; its overemphasis on exogenous forces at the expense of endogenous ones; its misrepresentation, in its classical form, of the effect of foreign investment on the periphery; its underestimation of the developmental prospects of the periphery; and its relative helplessness in understanding the nature and collapse of state socialist societies and the future prospects of socialism. I conclude with some suggestions for rebuilding world-systems analysis.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".