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Record W4229962179 · doi:10.7765/9781526130594

Orphan texts

2018· book· en· W4229962179 on OpenAlexaboutno aff
Laura Peters

Bibliographic record

VenueManchester University Press eBooks · 2018
Typebook
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)TreasureMutinyEmpireHistoryAdventureOrder (exchange)ColonialismNarrativeLiteratureGenealogyClassicsArtLawArt historyAncient historyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

This book argues that Victorian culture perceived the orphan as a scapegoat - a promise and a threat, a poison and a cure. It first establishes a discursive context in which to read the orphan figure as embodying a difference within the family. To do so, it describes the figure of Heathcliff in Wuthering Heights against a number of discourses - namely, those of the foundling, the orphan as foreigner, and the orphan as criminal. The book then looks at the role of the orphan and popular orphan adventure narratives in policing and extending empire. It considers Charles Dickens's 'The Perils of Certain English Prisoners, and Their Treasure in Women, Children, Silver and Jewels' within the context of both the Indian Mutiny of 1857 and Dickens's own imperial sympathies. The book also offers the historical context for the schemes adopted at the time for emigrating orphans. It focuses on the three main destinations -Bermuda, New South Wales and Canada - in order to consider the motivations behind the emigrating of orphans and the contemporary evaluations of it. In this historical context, the book positions Rose Macaulay's Orphan Island (1924), which in its Utopian framework poses problems for the both the rationale of the schemes and for current debates within post-colonial studies. It further looks at the exiling of difference, in George Eliot's Daniel Deronda and the return of the exiled orphan from the colonies to the heart of empire, London, in Dickens's The Mystery of Edwin Drood.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.219
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.043
GPT teacher head0.247
Teacher spread0.204 · 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.

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

Citations1
Published2018
Admission routes1
Has abstractyes

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