What Makes a Text a Magical Realist Work? - A Study Using Family Resemblance and Genre Theory
Bibliographic record
Abstract
Magical Realism is one of literatures most elusive and debated concepts, as it can be easily confused with other related genres. There is an ongoing debate about whether to label it as a genre or mode of narrative. Magical Realism is an International contemporary genre with its roots in Germany, while it became popularised and pioneered in South America. Over the years, critics have had issues defining the characteristics of Magical Realism as it subtly overlaps with other similar genres like surrealism, fantasy, science fiction, and gothic. Family Resemblance theory explains the overlapping resemblances of almost similar genres using Genre theory. This paper explores the binding relationship between Magical Realism and other genres. Later, they resemble yet differ in detail using their core characteristics. The paper also provides a comparative study on selected genres and studies the concept of genres, their construction, and their evolution. Textual analysis methodology is used in this article to understand the characteristics of magical realism in the novel Kafka on the Shore by Haruki Murakami. Further analysis of how the novel differs from the other genres is also studied using textual analysis. Genre theory can be analysed to trace the origin and evolution of a genre throughout the years and how they are arbitrary and are constantly misused by authors.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".