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Record W3087992299 · doi:10.6258/bcla.202004/pp.0002

Listening to (Talks with) Ghosts: Haunting in Margaret Sweatman\'s When Alice Lay Down with Peter

2020· article· en· W3087992299 on OpenAlexaboutno aff
Wang, Mei-Chuen

Bibliographic record

Venue臺大文史哲學報 · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningAlice (programming language)PsychologyHistoryArtVisual artsArt historyCommunication

Abstract

fetched live from OpenAlex

瑪格麗特.史威特曼的《當愛麗斯與彼得同眠》是一部將兩位蘇格蘭移民的家族史與1869年到1979年間加拿大重要的歷史事件相互羅織的歷史小說。這部小說借用部分鬼故事這個文類的敘事手法來增加加拿大情境裡鬼魅糾纏的層次。挪用這些鬼故事敘事手法時,史威特曼聚焦在見鬼者這個身份上,凸顯鬼魅糾纏的空間面向與經濟基礎,並將擁有與剝奪、佔有與驅逐的辯證關係用來重新檢視「1869-70年紅河抵抗」與「1885年西北叛變」兩次美蒂斯人(Metis)抗爭的歷史。本論文將探討史威特曼的小說如何將殖民與被殖民的二元對立複雜化成印地安人、美蒂斯人、白人移民三者間的土地爭奪戰,以及她的文本如何召喚讀者成為見鬼者,聆聽鬼魂敘述移居者國族主義希冀原住民化的不可得。

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)0.003
metaresearch head score (Gemma)0.006
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 score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0360.027
Scholarly communication0.0120.011
Open science0.0010.008
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0210.004

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 head0.012
GPT teacher head0.200
Teacher spread0.188 · 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
Published2020
Admission routes1
Has abstractyes

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