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Record W2806830890

"What to do with so much sorrow?". Art and the Purpose of Revenge in Margaret Atwood's Hag-Seed

2017· dissertation· en· W2806830890 on OpenAlexaboutno aff
Jessica Ann McDaid

Bibliographic record

VenueDipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) · 2017
Typedissertation
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsTempestSorrowWifePsychoanalysisArtContext (archaeology)NarrativeMoralityInterpretation (philosophy)Power (physics)GriefLiteratureReinterpretationArt historyAestheticsPhilosophyHistoryPsychologyLawTheologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Margaret Atwood's Hag-Seed is a postmodern, post-Christian reinterpretation of Shakespeare's Tempest, staging Felix Phillips as Prospero in the context of 21st century Canada. The nature of Prospero's morality has been an important subject in academic discussion, which can broadly be categorised into "the Artist" and "the Avenger". Atwood's allocation of Felix as an avenger and principle agent of his profane purpose, which entails a metafictional, druginfused production of The Tempest to cause his enemies to suffer in retaliation, provides reflections on the nature of revenge and morality in our current society. Furthermore, Felix's narrative incorporates two backstories which unfold the origin of his thirst for vengeance: his termination as Artistic Director ensued the traumatic loss of his wife and daughter. Felix will undergo a journey of healing through his artistic creativity culminating in his ability to face his grief and, ultimately, forgive himself. Therefore, in this present-day interpretation of The Tempest, art replaces the supernatural power to restore justice and grant absolution to its characters.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.038

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 studies0.0130.034
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.218
Teacher spread0.215 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona)Same topicShort Stories in Global LiteratureFrench-language works237,207