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 helped me handle many difficult moments and recover from many setbacks.David Cunningham and Peter Conrad set great examples of excellence in both teaching and research, and I am thankful for their generous time and insights.I was lucky to have Kathleen Jenkins and Kelly Joyce during my first job at the College of William and Mary; both were mentors who supported me selflessly.Bandana Purkayastha consistently reminded me that my book will be an impor tant contribution to the field; without her encouragement, I might have dropped this manuscript.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.320 | 0.209 |
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".