The Portrayal of Belonging and Unbelonging in David Chariandy’s Soucouyant
Bibliographic record
Abstract
David Chariandy’s Soucouyant, published in 2007, deals with immigrant characters trying to make sense of their identity and where they belong. However, there has not been a study particularly about belonging and unbelonging and how these experiences may resemble or vary among the characters. The thesis explores the characters’ experiences and their similarities and differences. The aim of this study is to explore the belonging and unbelonging of the first-generation immigrant character Adele and her second-generation immigrant son, who is also the nameless narrator of the novel.\nThe thesis consists of an introduction, a literature review, an empirical study, and a conclusion. The introduction gives a brief overview of the plot of the novel, its relevance in Canadian context and discusses the topics that are further explored in the thesis. The introduction also contains the aim of the thesis and the research questions. The first chapter is a literature review which gives an overview of previous research done on the novel, with a focus on topics such as identity and generation relationships, memory transmission, multiculturalism, and belonging and unbelonging. The second chapter, which is the empirical study, provides an analysis of both Adele’s and the narrator’s experiences and feelings with belonging and unbelonging. In addition, the similarities and differences of the experiences will be discussed. The conclusion provides a summary of the findings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".