Making Sense of Migration from the Other Side of the Ocean: Letters of the Families and Friends Who Remained Behind
Bibliographic record
Abstract
Carissima figlia, ho ricevuto la tua cara lettera e non sai quanta gioia ho provato nel sentire le belle notizie che mi hai dato, perché quando sei partita non ho fatto altro che piangere e pensare a te.Ora sono tranquilla e contenta nell'apprendere dalla tua lettera del viaggio tranquillo e della buona accoglienza da parte dei tuoi genitori.Anche per me sarebbe stata una grande gioia stare vicino a voi e partecipare alla vostra felicità.Io prego il Signore che mi dia la fortuna di poterti riabbracciare... Ti raccomando di essere buona e brava come sei stata con tua madre e di rispettare i tuoi genitori e di voler bene a tuo marito, perché nella vita l'unica felicità è volersi bene.Grazie delle mille lire che mi hai mandato.Il mio pensiero è sempre rivolto a voi e vorrei esservi vicino, ma la lontananza è troppa, quindi non è possibile.Tanti baci e abbracci a te e Domenico con la Santa Benedizione.Mamma My dearest daughter, I have just received your sweet letter and must tell you how happy I am to hear such good news.Since you left, I have spent my days in tears as I wondered about you.Now I feel at ease knowing that your trip went well and that your new parents have received you with open arms.I too would like to share in your happiness and pray to God that I may be fortunate enough to hold you again...
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".