A World-Systems Reader: New Perspectives on Gender, Urbanism, Cultures, Indigenous Peoples, and Ecology
Bibliographic record
Abstract
Chapter 1 Introduction: World-Systems Analysis: A Small Sample from a Large Universe Chapter 2 Recent Research in World-Systems Analysis Part 3 From Many Disciplines Chapter 4 Archaeology and World-Systems Theory Chapter 5 Geography & World-Systems Analysis Chapter 6 K-Waves, Leadership Cycles, and Global War: A Non-Hyphenated Approach to World Systems Analysis Chapter 7 Gender and the World-System: Engaging the Feminist Literature on Development Part 8 World-System Overviews Chapter 9 Canada's Linguistic and Ethnic Dynamics in an EvolvingWorld-System Chapter 10 Urbanization in the World-System: A Retrospective and Prospective Look Chapter 11 World-Systems Theory in the Context of Systems Theory: An Overview Chapter 12 Postmodernism Explained Part 13 Gender, Urbanism, Cultures, Indigenous Peoples, and Ecology Chapter 14 Women at Risk: Capitalist Incorporation and Community Transformation on the Cherokee Frontier Chapter 15 Resistance Through Healing among American Indian Women Chapter 16 World-Systems, Frontiers, and Ethnogenesis: Rethinking the Theories Chapter 17 Modern East Asia in World-Systems Analysis Part 18 Future Visions Chapter 19 Spiral of Socialism and Capitalism Chapter 20 World System and Ecosystem
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".