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Record W7052373954

Revelstoke e Non ho l’eta: dalle lettere alle docustorie migranti

2020· book-chapter· it· W7052373954 on OpenAlexaboutno aff

Bibliographic record

VenueCINECA IRIS Institutial Research Information System (University of Genoa) · 2020
Typebook-chapter
Languageit
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsPrivate educationState of artState (computer science)
DOInot available

Abstract

fetched live from OpenAlex

L'intervento propone l'analisi di due recenti documentari/"docustorie" realizzati dai registi Nicola Moruzzi e Olmo Cerri, impegnati nella ricostruzione di "percorsi migranti" che si snodano intorno alle lettere. "Revelstoke" è la storia di un giovane veneto emigrato in Canada ad inizio Novecento, bisnonno del regista, morto tragicamente durante la costruzione di una galleria ferroviaria senza poter vedere mai la figlia nata durante la sua assenza. "Non ho l'età" ricostruisce percorsi di vita partendo dalle missive di alcuni emigranti italiani in Svizzera, scritte negli anni Sessanta alla nota cantante Gigliola Cinquetti. In entrambi i casi le lettere funzionano da strumenti narrativi capaci di far compiere allo spettatore - ma in alcuni casi agli stessi autori delle missive o ai loro congiunti - un vero viaggio a ritroso nel tempo, in sè stessi e nel futuro: come se le missive fossero state nuovamente recapitate a decenni di distanza...(E' prevista la proiezione di alcune parti delle "docustorie")

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.065
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.008
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0140.003

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.

Opus teacher head0.048
GPT teacher head0.256
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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