Bibliographic record
Abstract
Historical texts are supposed to reflect the whole past; unfortunately, historical texts are not really faithful to the true past due to historians’ emplotment. Atwood thus calls into question that history is built on objective written documents. The written records concerning the murder and the historical Grace Marks also become highly questionable and unreliable. My thesis will not only tries to rethink how history is provided on the basis of events, but also to question how events are emplotted to give meaning. Moreover, I would like to examine how Atwood deploys emplotment and delivers meanings by virtue of storytelling in Alias Grace. The subject will be considered under the following four chapters including Chapter one: introduction, Chapter Two: juxtaposition of texts, Chapter Three: the symbol of quilting, and Chapter four: conclusion. Chapter one will introduce Alias Grace and Atwood’s essay, “In search of Alias Grace: on Writing Canadian Historical fiction,” and highlights the problematics of the unreliability of given documents. Chapter two examines what kinds of factors cause the differences and unreliability of documents. Chapter three elaborates on the symbol of quilting to unravel how Grace uses storytelling to survive. Chapter four recapitulates my thesis and explains why Atwood re-emplots Grace’s events and composes Alias Grace.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".