Bibliographic record
Abstract
Vietnam veteran Howard Walker is fleeing from a dark secret.As 62-year-old Walker drives a coach bus across America, he recounts his experiences of war trauma and his bond with the famous protest singer Philip Boothman back in New York City. Boothman pulled Walker out of the gutter, and in return, Walker has been handling shady business for the renowned songwriter. As Walker’s story unfolds, the truth about Boothman\'s ill-fated romantic affair with a Danish girl comes to light"The Road to Alberta" is a sombre, contemporary portrayal of America, reminiscent of "The Great Gatsby." The novel beautifully explores the intricate dynamics of striving for a dream while navigating the complexities of unreciprocated love. A narrative of betrayal, friendship, commitment, art, and disillusionment.“A well-composed novel riddled with unfulfilled dreams, broken hearts, and human fate When Brian Dan Christensen leads you around New York City, you feel just as safe as when Ellroy gives you a guided tour of L.A. or Elmore shows you Detroit.” LitteratursidenBrian Dan Christensen (b. 1970) is a Danish-American writer and translator. He has published novels, poetry, and nonfiction, has translated such diverse writers as Garrison Keillor, Christopher Isherwood, Jack Kerouac, Graham Greene, Norman Mailer, David Nicholls, and Catherine Lacey, and has appeared on A Prairie Home Companion.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.027 |
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".