“Is all o’ we one?”: Creolization and ethnic identification in Samuel Selvon’s “Turning Christian”
Bibliographic record
Abstract
Samuel Selvon’s fiction reveals the author’s abiding concern with questions of identity and community and his investment in reconciling the seemingly conflicting subjects of creolization and ethnic identification in Caribbean societies, particularly in his native Trinidad. The pervasive and often violent ethnic conflict between Trinidadians of Indian and African heritage is linked to constructions of the nation in which claims to, as well as exclusion from, Creole identities play an important role. In response, Selvon’s fictional interventions position Indian communities (whether peasant, working- or middle-class) in relation to other ethno-racial groups in ways that construct Trinidadian-ness as an inclusive and dynamic negotiation of self and culture across the various communities represented in the nation. Drawing on Kamau Brathwaite’s seminal concept of creolization as well as the work of other theorists (including Mintz, Bolland, and Munasinghe) of Creole identities and the creolization process, the analysis of “Turning Christian” — a short story excerpted from Selvon’s unfinished novel — provides an account of Selvon’s identity politics in this and his other works of fiction.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".