Bibliographic record
Abstract
Drawing on textual modes of fictional representation first valorised then disseminated by British imperialists, this story appropriates then abrogates Eurocentric ideals of normative creative expression. By appropriating the rhythm of lyrical poetry into the descriptions of this fictionalized Jamaica as well as the structure of modernist prose into Akua’s psychical shift from colonial subject to postcolonial agent, “Me Nuh Choose None” inverts these narrative conventions historically used to systematize and exclude in order to puncture the imperious myth that complex writing in English primarily concerns itself with gratifying the colonial centre. Moreover, by treating the textual approximations of the Jamaican storyteller Miss Lou’s oral folktales with equal depth and sophistication as the instances of alliteration and onomatopoeia, this story abrogates the autocratic standard of “proper” spelling and pronunciation so as to textually privilege Jamaican folklore using the literariness usually reserved for Western texts. The effect is a form of fiction that acknowledges the dense matrix of cultural and colonial systems English writing exists within, while simultaneously gesturing between and beyond these systems to the linguistic interstices of postcolonial liminality.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".