Crossing Borders, Advancing Scholarship
Bibliographic record
Abstract
Spanning personal narratives and scholarly inquiry, this volume presents thirteen autobiographical accounts from sociologists of Chinese descent whose research is deeply informed by their lived experiences as immigrants or children and grandchildren of immigrants. This groundbreaking collection offers a rare and illuminating perspective on how migration shapes research trajectories, theoretical frameworks, and knowledge production. Through these intimate reflections, contributors explore the methodological challenges of studying communities in which they occupy the dual position of insider and outsider. Their accounts reveal the intersections of personal history and professional development, highlighting the broader social and political contexts – spanning mainland China, Hong Kong, Taiwan, and migrant host societies such as the United States and Canada – that have influenced their careers. Crossing Borders, Advancing Scholarship offers both a methodological toolkit and a thought-provoking exploration of how diasporic lives shape scholarly work. It not only deepens our understanding of diasporic identity but also serves as an inspiring guide for emerging researchers navigating the complexities of academia and ethnic belonging.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.072 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".