Bibliographic record
Abstract
This interview is included in the American Folklore Society Oral History Project held at the Archive of Folk Culture, American Folklife Center, Library of Congress, Washington, D.C. This item is two interviews with Archie Green conducted by David A. Taylor of the American Folklife Center. Archie Green discusses his family history, his parents' immigration from the Ukraine to Canada and then to Los Angeles; his education at the University of California, Berkeley; his work as a surveyor for the CCC; his work as a shipwright, and his brief tenure as a labor union official during World War II. He enlisted in the Navy, was assigned to a ship repair unit, served in a drydock in San Diego and in Albany, California, then in the eastern Philippines and China, coming home in 1946. He discusses folk music and folksongs, the folk music revival, Young Democrats, the working classes, advocacy for vernacular culture, and his activities lobbying for the passage of the Congressional act that established the American Folklife Center at the Library of Congress. This collection consists of 4 sound cassettes : analog, stereo. Recorded Dec. 15-16, 2003 at Archie Green's home in San Francisco, Calif. Biography/History note: Archie Green was a folklorist and activist, born June 29, 1917 in Winnipeg, Ontario; died March 22, 2009 in California. He received the Library of Congress Living Legend Award on August 16, 2007.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.115 | 0.034 |
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".