Bibliographic record
Abstract
This article provides a cultural history of book clubs in Nigeria and situates this reading tradition within the context of Anglophone colonial legacies and contemporary Yorùbá language politics. The case study is the Ẹgbẹ́ Àtẹ́lẹwọ́ group, a Yorùbá book club, and their associational life, which began on the Zoom platform in 2020 and later migrated to Twitter in 2021. The argument draws inspiration from the work of Adélékè Adéẹ̀kọ́, who conceptualises epistemic decolonisation of language as foundational to all decolonial practices. The digital reading practices of Àtẹ́lẹwọ́ book club enable a necessary disruption to literary experience in Nigeria, which is still largely dominated by Anglophone literary legacies. This book club develops a model for readers to participate in decolonial processes, and opens a path for social and cultural development in Nigeria.
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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 | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".