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Record W3160448425 · doi:10.5539/ells.v11n2p50

Philip Sidney’s Stella: The Lady, the Countess, and the Queen

2021· article· en· W3160448425 on OpenAlexvenueno aff
Saleh H. Alkharji

Bibliographic record

VenueEnglish Language and Literature Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicNarrative Theory and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSTELLA (programming language)RomanceContext (archaeology)MonsterPoetrySonnetReading (process)Art historyArtLiteratureHistoryPhilosophy

Abstract

fetched live from OpenAlex

In his poetic sequence, Astrophil and Stella (1591), Philip Sidney dramatizes his speaker’s romantic ambitions of climbing the Ladder of Love. While many academics interpret the sequence as a semi-biographical work, they disagree in evaluating how deep the sequence mirrors Sidney’s life. Traditionally, Astrophil is interpreted as a surrogate for Sidney and, more critically, Stella is read as a fictionalized version of Lady Rich. However, given the inconsistency of literary evidence, a new reading of the sequence emerged and argued that Stella is Sidney’s wife, Frances Walsingham. Although this paper agrees on the surrogacy of the speaker in the sequence, a closer analysis of the poetic language used in Sidney’s sonnets would contradict these Stella’s interpretations. Furthermore, as this paper cites historical documents that confirm the non-romantic relationship between Philip Sidney and Lady Rich, a closer examination of the sequence and the historical context of the Elizabethan Era would conclude that Stella’s real identity is far more complex and multidimensional than to be a mere fictionalized version of Lady Rich or Frances Walsingham. In fact, an investigation of Sidney’s personal life and a close reading of Astrophil and Stella would conclude that Sidney’s Stella is a masked version of Queen Elizabeth.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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 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
Published2021
Admission routes1
Has abstractyes

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