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Record W2907960036 · doi:10.12745/et.21.2.3607

Emotions in <i>The Witch of Edmonton</i>

2018· article· en· W2907960036 on OpenAlexaffvenueabout
Kathryn Prince

Bibliographic record

VenueEarly Theatre · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Emotions Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSophisticationWitchUncannyPleaCharacter (mathematics)AestheticsArtSociologyPsychoanalysisLiteraturePsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In The Witch of Edmonton, a diabolical dog escapes from the supernatural subplot to unleash mischief, madness, and murder in rural Middlesex. While the play’s emotional sophistication is easy to overlook because of the preposterousness of a costumed actor taking the stage as a talking dog, an analysis grounded in History of Emotions approaches and focusing on Dog reveals the extent to which this play, in dramatizing a society without charity, makes a convincing emotional plea centred on the emotions that mobilize, and are mobilized by, the uncanny character at its heart.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.014
Scholarly communication0.0060.002
Open science0.0000.002
Research integrity0.0010.003
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 head0.032
GPT teacher head0.253
Teacher spread0.221 · 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
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
Published2018
Admission routes3
Has abstractyes

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