Bibliographic record
Abstract
A historic summit of two groups of WWII veterans that faced discrimination: the Tuskegee Airmen and Chinese-Canadian soldiers. Meeting for the first time ever, these aging veterans will share their stories with the public on how they overcame prejudice to serve their countries with courage and distinction. The Tuskegee Airmen are African-American pilots who fought in World War II. Formally, they formed the 332nd Fighter Group and the 477th Bombardment Group of the United States Army Air Corps (United States Army Air Forces after 20 June 1941). While most of their ranks have passed away, a few remaining veterans, now mostly in their late 80s and 90s, will meet to share their stories. During WWII, the Tuskegee airmen were the first group of African-American aviators to fly in combat for the US armed forces. At the time, the American military was still racially segregated. Many felt African-Americans lacked the intelligence and skill to perform anything beyond basic, menial tasks in military duty. Despite this segregation and prejudice, the Tuskegee Airmen went on to become one of the most highly respected fighter groups in the war. They were dubbed “the Red Tails” after one fighter group painted their P47s and later P51s with a red tail. Please join us for this historic occasion. This UBC opening symposium took place on June 28, 2013, 2013 at the Victoria Learning Theatre (Room 182), Irving K. Barber Learning Centre. Panelists: Col. Charles McGee, Lt. Robert Ashby, Bill Norwood, Col. Dick Tolliver (Tuskegee Airmen); Col. Howe Lee, George Chow, Monty Lee, Frank Wong (Chinese-Canadian Veterans); Moderated by Don Chapman
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".