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Record W6990190839

Crossing the Atlantic at the turn of the Millennium: friends and foes

2022· book-chapter· it· W6990190839 on OpenAlexaboutno aff

Bibliographic record

VenueNova Science Publishers (Nova Science Publishers, Inc.) · 2022
Typebook-chapter
Languageit
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLingua francaLarge group
DOInot available

Abstract

fetched live from OpenAlex

Il saggio analizza le modalità dell'incontro tra un gruppo di Vichinghi e le popolazioni indigene delle coste orientali del Canada al momento della scoperta dell'America, intorno all'anno 1000, come narrato dalle saghe islandesi che ci hanno conservato la memoria di questi avvenimenti (Vinland Sagas). Documenti situabili a metà strada tra resoconti attendibili e fiction - un po' per la loro natura, un po' per le caratteristiche della trasmissione culturale norrena, e un po' per la distanza temporale tra gli eventi narrati e la loro registrazione in forma scritta - le saghe ci narrano di incontri e interazioni mossi principalmente da curiosità e desiderio di commerciare. Gli scontri, che pur avverranno e che porteranno poi i Vichinghi ad abbandonare questi territori, sembrano dettati più da paura e diffidenza scaturite da avvenimenti fortuiti ed imprevedibili, che da conflittualità predeterminata e sete di conquista. Nonostante la mancanza di una lingua e di un sistema culturale di riferimento comuni, i due gruppi riescono in più occasioni, come mostra la disamina, a superare le differenze e trovare un sistema di comunicazione efficace. E il nemico reale, anzichè vestire colori etnici, avrà i connotati dell'avidità e dell'inganno serpeggianti all'interno del gruppo vichingo stesso.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.007
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.042
GPT teacher head0.287
Teacher spread0.245 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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