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Record W2344851297 · doi:10.14288/1.0076737

Red Tails and Dragon Tales

2013· article· en· W2344851297 on OpenAlexaboutno aff
Don Chapman

Bibliographic record

VenuecIRcle (University of British Columbia) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsHistory

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.183
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicWorld Wars: History, Literature, and ImpactFrench-language works237,207