Tienshi Lara Chen Stateless: Translated by Louis Carlet, National University of Singapore Press Tienshi Lara Chen Stateless: Translated by Louis Carlet, National University of Singapore Press (2023).
Bibliographic record
Abstract
supremacy in the field of sports, Filipino American Sporting Cultures attempts to demolish the asymmetries based on race, gender, sexuality and class that exist between and among Filipino-American athletes playing in America and Filipinos in diaspora.The book makes a valuable contribution to migration studies by providing intersectional perspectives in its interrogation of Filipino-American sporting cultures.It offers ways forward on how to overcome the lack of acceptance and recognition of Filipinos in diasporic migrant communities through homosocial intimacies.Finally, it develops "crossover" as an innovative lens to highlight how migrant and diasporic Filipinos navigate the difficult politics and realities of identity, belonging and community in the US through sporting culture.However, as Asian-Americans appear in some of the chapters, it might have been useful to clearly delineate the politics of differences and lived experiences between Filipino-Americans and Asian-Americans.A separate chapter that decouples the underlying similarities and differences of Filipino Americans to some Asian-American athletes along racial, gender, sexuality and class politics of play might have further strengthened the book.In the end, the book challenges the gender and sexual stereotypes, social labels and cultural stigmas that beset Filipinos in the Philippines and in the diaspora, underscoring the relevance of crossover from these limitations and constraints for self-acceptance and self-worth.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.170 | 0.116 |
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".