Bibliographic record
Abstract
The Whale Chaser is the story of Vince Sansone, the eldest child and only son in a large Italian American family, who comes of age in 1960s Chicago. A constant disappointment to his embittered father - a fishmonger who shows his displeasure with his fists - Vince finds solace by falling in love. Classmate Marie Santangelo, the butcher's winsome daughter, entices him with passionate kisses and the prospect of entering her family's business. Yet he pursues Lucy Sheehan, an older girl with a reputation. When Vince abruptly flees Chicago, he ends up in Tofino, a picturesque fishing town on the rugged west coast of Vancouver Island in British Columbia. He finds a job gutting fish, then is hired by Tofino's most colorful dealer, Mr. Zig-Zag, and joins the thriving marijuana trade. Ultimately, through his friendship with an Ahousaht native named Ignatius George, he finds his calling as a whale guide. Set in the turbulent decades of the Vietnam War and the drug and hippie counterculture, The Whale Chaser is a powerful story about the possibility of redemption.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".