Bibliographic record
Abstract
In some segments of the urban West African population today, fictional narratives created locally are more likely to be watched on video film rather than read in a book or listened to on radio. Tunde Kelani, considered one of the most successful video film directors in Nigeria, and who has adapted the works of at least one Yoruba-language author to video film, reveals his awareness of this trend in the following statement: We found that our people are no more interested in reading, and by so doing, we are missing the great values and virtues embedded in the works of great writers... (Abiola 14). Lower literacy rates alone do not account for the apparent lack of interest in literature. 1 In the wake of structural adjustment programs pursued in many countries, the accumulation of wealth and access to leisure time is not as clearly tied to advanced education and a personal investment in reading. African publishers also face considerable challenges in making noncommercial fiction available to local audiences. In many instances, the secular non-commercial fiction book is both unavailable and unaffordable. 2
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".