Bibliographic record
Abstract
1. Migration, Political Economy and Ethnography Pauline Gardiner Barber and Winnie Lem Part I: Perspectives 2. Panoptics of Political Economy: Anthropology and Migration Winnie Lem 3. Migration and Development Without Methodological Nationalism: Towards Global Perspectives on Migration Nina Glick Schiller 4. Theorizing Transnational Movement in the Current Conjuncture: Examples from/of/in the Asia Pacific Donald M. Nonini Part II: Cases 5. With Crossings In My Mind: Trinidad's Multiple Migration Flows, Policy and Agency Belinda Leach 6. Selecting, Competing, and Performing as 'Ideal Migrants': Mexican and Jamaican Farmworkers in Canada Janet McLaughlin 7. In Search of Hope: Mobility and Citizenships on the Canadian Frontier Lindsay Bell 8. Constructing a Perfect Wall: Race, Class, and Citizenship in U.S.-Mexico Border Policing Josiah McC. Heyman 9. The Aftermath of a Rape Case: The Politics of Migrants' Unequal Incorporation in Neo-Liberal Times Bela Feldman-Bianco 10. Gender, Migration and Rural-Urban Relations in Postsocialist China Yan Hairong 11. Value Plus Plus: Housewifization and History in Philippine Care Migration Pauline Gardiner Barber and Catherine Bryan 12. Migration, Political Economy and Beyond Pauline Gardiner Barber and Winnie Lem
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".