Bibliographic record
Abstract
If my grandparents planted the so cio log i cal seeds in my heart, many people have helped me grow, cultivate, and harvest the fruit.This book would not have been pos si ble without the respondents who generously shared their life stories with me.For ethical reasons, I cannot thank them here individually, but I appreciate their generosity in opening themselves and, in many cases, their homes to me.I am also deeply grateful for the inspiration they provided.This book began at Brandeis University, and my colleagues there provided valuable feedback that shaped its development.I am blessed to have had Karen V. Hansen as my mentor.Karen had incredible faith in me, more than I sometimes have in myself.She reassured me during periods of self-doubt and guided me through difficult professional transitions.I want to thank Wendy Cadge for teaching me to or ga nize data, construct a so cio log i cal argument, and navigate the discipline.She has always been there for me.I am grateful as well to Sara Shostak for teaching me to be a professional sociologist; as a gradu ate student, I secretly dreamed about being as sharp as her one day.I also extend my sincere gratitude to Nazli Kibria, who was generous with her knowledge, insights, and professional connections, even when she was stretched thin with professional and personal commitments.I am indebted as well to Mary C. Waters for her insightful comments.She is the first person who alerted me to the potential for bridging the areas of aging and international migration.I also wish to extend my gratitude to a few people who provided critical support for this book and for my professional development.Nadia Kim introduced me to the field of migration and taught me the importance of thinking about the complexity of race/ethnicity, class, and gender.From the first day of gradu ate school, Laura Miller
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".