Bibliographic record
Abstract
Censorship and the Content of Nigerian Video Films Abstract: Because the field of culture has always been considered a powerful socio- political and economic endeavor, it has often been a site of keen interest by the state. From the very inception of the creative enterprise the world over therefore, especially within the ambit of contemporary nation states, literary and other imaginative reproductions of culture have always attracted some kind of surveillance. But these regulatory practices, and their implications for the content of culture have differed from place to place and from time to time. In this paper then, I propose to examine the specific nature of censorship within the bourgeoning Nigerian video film industry and, the implication of such unique censorship for what we see in the films. As a point of entry into that problematic, I track the beginnings of film censorship within the Nigerian state enfolding in the process the statutory bodies and laws that had [and are] involved in these regulatory activities. I shall then proceed to examine the actual formal process of state censorship and, the hidden forms of censorship within the video colony. By problematizing this regulatory process, I hope to illuminate how the unique forms of censorship within the video industry impinge on what we see [and do not see] in the films. Paul Ugor Department of English and Film Studies University of Alberta, Edmonton Canada. Phone: 01-780-492-7833.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".