Mixanthropic characters and their role in the graphic novel ‘Angel Catbird’ by M. Atwood
Bibliographic record
Abstract
The aim of the study is to determine why the famous Canadian writer Margaret Atwood turns to mythological characters that have a hybrid, or mixanthropic, appearance in her graphic novel ‘Angel Catbird’ (2016). The paper examines the history of the development of the comic book genre and its special format, the graphic novel; emphasis is also placed on the different attitudes to this genre among Russian and foreign researchers. Moreover, the features of M. Atwood’s graphic novel ‘Angel Catbird’ and its differences from other works in this genre are considered. The study concludes with an analysis of the nature of the main characters and their connection not only with human nature (in a narrow sense), but also with the peculiarities of the development of Canadian literature and culture in general (in a broad sense). The scientific originality of the study lies in the fact that the paper considers the system of images of the novel ‘Angel Catbird’ from the perspective of the duality of their consciousness and mixanthropic appearance. In addition, it is the first time that M. Atwood’s graphic novel has been analysed taking into account the use of mythological images and traditions of different peoples. As a result, it has been proved that the use of mixanthropic characters in the graphic novel is linked to the multifunctionality of their images.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".