Bibliographic record
Abstract
1. Methodological Predicaments of Cross-Border Studies Anna Amelina, Thomas Faist, Nina Glick Schiller and Devrimsel D. Nergiz Part I: Researching International Migration after Redefining Spatiality and Mobility 2. Transnationality, Migrants and Cities: A Comparative Approach Nina Glick Schiller 3. Transnational Migration and the Reformulation of Analytical Categories: Unpacking Latin American Refugee Dynamics in Toronto Luin Goldring and Patricia Landolt 4. Overcoming Methodological Nationalism in Migration Research: Cases and Contexts in Multi-Level Comparisons Anja Weiss and Arnd-Michael Nohl Part II: Material, Culture and Ethnicity: Overcoming Pitfalls in Researching Globalization 5. Global Ethnography 2.0: From Methodological Nationalism to Methodological Materialism Zsuzsa Gille 6. Uncomfortable Antinomies: Going Beyond Methodological Nationalism in Social and Cultural Anthropology David Gellner 7. Approaching Indigenous Activism from the Ground Up: Experiences from Bangladesh Eva Gerharz Part III: Juxtapositions of Historiography after the Hegemony of the National 8. The Global, the Transnational, and the Subaltern: The Limits of History Beyond the National Paradigm Angelika Epple 9. Incorporating Comparisons in the Rift: Making Use of Cross-Place Events and Histories in Moments of World Historical Change Sandra Curtis Comstock 10. Interrogating Critiques of Methodological Nationalism: Propositions for New Methodologies Radhika Mongia Part IV: Conclusions 11. Transnational Social Spaces: Between Methodological Nationalism and Cosmo-Globalism Ludger Pries and Martin Seeliger 12. Concluding Remarks: Reconsidering Contexts and Units of Analysis Thomas Faist and Devrimsel D. Nergiz
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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 | 0.157 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.014 | 0.127 |
| Scholarly communication | 0.023 | 0.030 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".