Bibliographic record
Abstract
Early in his judicial career, U.S. District Judge Warren K. Urbom was assigned a yearlong string of criminal trials arising from a seventy-one-day armed standoff between the American Indian Movement and federal law enforcement at Wounded Knee, South Dakota. In Called to Justice Urbom provides the first behind-the-scenes look at what quickly became one of the most significant series of federal trials of the twentieth century. Yet Wounded Knee was only one set of monumental cases Urbom presided over during his years on the bench, a set that in turn forms but one chapter in a remarkable life story. Urbom’s memoir begins on a small farm in Nebraska during the dustbowl 1930s. From making it through the Great Depression and drought to serving in World War II, working summers for his father’s dirt-moving business, and going to school on the G.I. Bill, Urbom’s experiences constitute a classic American story of making the most of opportunity, inspiration, and a little luck. Urbom gives a candid account of his time as a trial lawyer and his early plans to become a minister—and of the effect both had on his judicial career. His story offers a rare inside view of what it means to be a federal judge—the nuts and bolts of conducting trials, weighing evidence, and making decisions—but also considers the questions of law and morality, all within the framework of a life well lived and richly recounted.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.419 | 0.191 |
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".