Listening to (Talks with) Ghosts: Haunting in Margaret Sweatman\'s When Alice Lay Down with Peter
Bibliographic record
Abstract
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".