Doug Dobransky, A Year that Changed a Lifetime
Bibliographic record
Abstract
[profile bio] Born in Butler, Pennsylvania and raised in Youngstown, Ohio, Doug Dobransky was drafted out of high school and into the Army at the age of 19. An expert marksman, he placed at the top of his training class at Fort Polk, Louisiana before being deployed to Vietnam in January 1967. He served his year, partly as an infantryman in the 199th brigade and the remainder as a writer of casualty reports. He returned home in 1968 and moved to southern California, where he has lived since. Doug has been working as a Hollywood photographer for the last thirty years, and his impressive list of clients includes Marlon Brando, Muhammad Ali, Mickey Rourke, David Hasselhoff, Brad Pitt, Ted Danson, and countless other celebrities. He is also the author of Autumn Sister, a very personal, intimate, and touching journal of his experience with his sister's battle with cancer. [profiler bio] Tony Hsieh, Eric Hsu, and Alvin Liu are all students at the University of Southern California.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.163 | 0.210 |
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".