Bibliographic record
Abstract
I knew from an early age that aloneness would be a lingering element in my life.Despite being rather popular at school and later at work becoming sort of a leader of a large group of young people who gathered to have fun on weekends or other social occasions, I felt more affinity with the characters I met reading the novels I methodically picked from the family library we had in our home in Florence than I did with the real flesh and blood people I knew.With the power of memory and imagination, I can still recreate the appearance of the bookshelves in our lounge, with their orderly rows of book collections: the grey covers of the cheap paperback classics by Biblioteca Universale Rizzoli, the ivory coloured book jackets of the hard cover Einaudi publications, the names of authors and titles embossed in golden letters, the worn-out brown leather spines of my grandfather's books by poets and historians of ancient Rome, written in classic Latin.I spent hours and long, silent Sundays in a communion of spirit with Rodion Raskolnikov,!Amaranta Ursula Buendfa,2 Charles Marlow? or Jeanne de Lamare* whose final exchange of words with Rosalia, her former servant at the end of A Woman's Life, I still repeat to myself even after so many years: "You see, life is neither as good nor as bad as we think."I read books without plan or guidance, transported by a passion that only my grandfather seemed to understand while my parents worried more about how too much reading would end up making me blind and giving me a crooked back.I started to dream that one day I would 1 Rodion Romanovitch Raskolnikov is the fictional protagonist of Crime and Punishment by Fyodor Dostoyevsky.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.029 | 0.018 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 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".