Animal representations in Margaret Atwood’s novels: a study based on pan-indexicality model
Bibliographic record
Abstract
Abstract Margaret Atwood is a Canadian author of more than thirty-five books and the winner of prestigious literary prizes, such as the Booker Prize, the Giller Prize, and the Governor General’s Award. Her influence on Canadian literature and contemporary literature as a whole is phenomenal. Nevertheless, little is known with respect to how Atwood represents animals covering the full range of her novels. This paper reports on the analysis of animal representations in Atwood’s seventeen novels through Python programming and close reading under the framework of a new semiotic research finding, a pan-indexicality model within the context of literature and the environment. This study investigates the frequencies of animal vocabulary in the seventeen novels, the changes of animal representations in her novels before 1990s and after 1990s, and the implication of the ever-changing animal representations during the fifty years. This paper concludes that nonhuman animal descriptions in Atwood’s novels of 1970s and 1980s run at a high level and decrease in her novels of 1990s, while scientific animal descriptions increase in her novels of 2000s and 2010s. Nonhuman animals in her novels of 1970s and 1980s are instrumentalized as a vehicle for indigenization and national individuation from the United States, and scientific animals in her novels of 2000s and 2010s are instrumentalized in the service of environmental apocalypticism. This study suggests that the pan-indexicality model can be employed to understand the meaning of signs in literature and the environment from the perspective of authorial intention, with reference to authors’ encyclopedic knowledge, personal experience, social, and cultural background information.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".