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Record W2949444704 · doi:10.12745/et.22.1.3624

Marlowe and Shakespeare Cross Borders: Malta and Venice in the Early Modern World

2019· article· en· W2949444704 on OpenAlexvenueno aff
Shormishtha Panja

Bibliographic record

VenueEarly Theatre · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
Fundersnot available
KeywordsSiegeDepictionVictoryOpposition (politics)NarrativeMythologyLiteratureArtHistoryAncient historyLawPoliticsPolitical science

Abstract

fetched live from OpenAlex

This essay deals with the worlds of early modern Malta and Venice, two distinctly non-English locations, as depicted by Marlowe and Shakespeare. In particular, it considers the roles Jews played in The Jew of Malta and The Merchant of Venice. I argue that while Shakespeare is completely accurate in his depiction of the spirit of financial and mercantile adventurism and huge risk-taking that characterized early modern Venice, he does not fully reflect the tolerance that marked this early modern trading capital. Shakespeare bases his play on binaries and antagonistic opposition between the Jews and the Christians in Venice while Marlowe consciously resists painting his world in black and white. Marlowe’s Malta is a melting pot, a location where boundaries and distinctions between Jew, Christian, and Muslim, and between master and slave, blur, and easy definitions and categorizations become impossible. In spite of borrowing many historical details of the Great Siege of Malta (1565), Marlowe refuses to end his play with the siege and its attendant grand narrative of heroic Christian troops defeating barbaric Turks and bringing about a decisive victory for the Christian world.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.275
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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