Bibliographic record
Abstract
Stephen Sanderson’s (2005) ‘World-systems analysis after thirty years: Should it rest in peace?’ raised the prospect of an area of scholarship that had run its course. We answer the five main criticisms that he asserts against world-systems analysis: the primacy of exogenous over endogenous forces; teleology and reification; an incorrect understanding of the role of foreign investment; an inaccurate analysis of long-term trends of inequality; and, a misinterpretation of state socialism. As we respond to his criticisms, we find that while some of his arguments have merit, particularly against the relatively narrow form of world-systems analysis that he considered, his assumption of methodological individualism runs counter to the epistemological position of most world-systems scholars. Our review of the field finds it to be evolving and expanding into new realms that no do not suffer from the deficiencies Sanderson identified. Indeed, now at 35 years and counting, world-systems analysis is not dying, it is thriving.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.027 | 0.039 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".