Bibliographic record
Abstract
For the past year I’ve been assisting Doc- UWM in the production of a Documentary about Art Shay. Mr. Shay is a prolific Chicago based photographer who has captured some of the most historic events and figures of the 20th century. I’ve been tasked with studying the time in Mr. Shay’s life when he served in the 8th Air Force as a bombardier navigator. During this period, Mr. Shay was under the command of Colonel James Stewart and took part in thirty bombing missions over Nazi occupied Europe. After his first combat tour Mr. Shay went on to fly unmarked planes into neutral Sweden to provide Red Cross supplies to the Americans stranded there. Once the war had ended Lt. Shay continued to serve in the Air Force, flying V.I.Ps around the world. On one of these flights his plane was caught up in a snowstorm and was forced to crash in Newfoundland. The photos that Mr. Shay took during this incident helped jumpstart his career after he was rescued. I’ve gathered these stories through hours of interview footage with Mr. Shay and have tried to bring them to live with archival footage and the photographs personally taken by Shay during this tumultuous period. Hopefully once this project is completed the public will also be able to experience these important stories for themselves.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.815 | 0.718 |
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".