Bibliographic record
Abstract
The novel takes its title from Blood of My Blood: The Dilemma of Italian-Americans, Richard Gambino's 1974 sociological study.Sangu du me sangu, of course, refers to the primacy of blood relations in Italian culture.The novel also plays off against the 1968 humour classic How to be an Italian by Lou D'Angelo.Blood of My Blood is written as a film text (combination of script and prose) because of the main character's obsession with film, especially the work of Martin Scorsese and Francis Ford Coppola.The excerpted scene, "Sound of O," illustrates how the world of these movies, especially their depiction of what it means to be an Italian man, is very real to Gino Della Rocca.From Part II -"Sound of O" INT.DELLA ROCCA AUTO REPAIR SHOP -AFTERNOON The cavernous garage seems vast and empty.Pigeons squawk on the sills of opened rectangular windows lining the top of the eastside wall.Streams of sunshine dissipate before reaching the floor.Metal, rubber and grease in shades of grey, black, brown, khaki.Echoes of clanging metal, pumping, drilling.CARMELO and ENRICO work on cars in various states of disrepair; a younger boy, EUGENIO, changes a tire.Stitched onto their overalls in fancy script: GDR&S.They work close to each other Maria Francesca LoDico and banter jovially.CARMELO: ... my ass.That'
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 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".