Bibliographic record
Abstract
With the same ability to make personalities and events come alive that characterizes his classic Skid Road , Murray Morgan here tells the colorful story of Tacoma, “the City of Destiny,” and southern Puget Sound, where many major events of Washington’s history took place. Drawing upon original journals and reports, Morgan builds Puget’s Sound around individuals, interweaving portraits of well-known historical figures with a raucous parade of saloonkeepers, politicians, union organizers, schemers, and swindlers. His account begins with the landing of Captain Vancouver in Puget Sound in 1792 and ends with the founding of Fort Lewis in 1916. Between are the arrival of the transcontinental railroad, the boom-and-bust of lumber mills, the anti-Chinese riots of 1885, and more distinctive Northwest history that will intrigue both new arrivals and longtime residents. With a new introduction by historian and historic preservationist Michael Sean Sullivan, this redesigned edition of Puget’s Sound brings new life to Morgan’s landmark history.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.020 |
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".