Heather Stur. <i>Saigon at War: South Vietnam and the Global Sixties</i>.
Bibliographic record
Abstract
Heather Stur’s Saigon at War offers a captivating narrative of political activism in Saigon, the capital city of South Vietnam (Republic of Vietnam, RVN). Based on US and Vietnamese archival sources, and enlivened by some personal interviews, this book has much to offer. Stur brings to light one important aspect of the Vietnam conflict, the political war that unfolded in the cities, one in which Vietnamese were deeply engaged throughout the 1960s and 1970s. After providing an engaging introduction to Saigon’s sociopolitical situation, Stur devotes subsequent chapters to Vietnamese political activism after 1963. The book highlights individuals such as communist-affiliated student leaders Huynh Tan Man and Tran Huu Thai, rights activist Madame Ngo Ba Thanh, prison-reform advocate Reverend Chan Tin, anticorruption leader Reverend Tran Huu Thanh, and liberal-democrat diplomat Vu Van Thai. These and many more persons featured in the book were occupied with the fate of South Vietnam and worked tirelessly, sometimes risking jail time and their lives, to make their voices heard. Stur maintains that it was this segment of the population whose views mattered and whose support was critical for the survival of South Vietnam. It was these urban activists that the United States, the RVN government, and communist-led National Liberation (NLF) needed to win over rather than the supposedly apolitical rural population. Stur does not explicate how urban support might have changed the outcome of the war nor does she discuss the evidence for the belief that peasants were apolitical or that their support would not have mattered.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.029 |
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".