Bibliographic record
Abstract
kenneth Little’s less known but very incisive study, The Sociology of Urban Women’s Images in African Literature, provides a useful sociological guide to the study of women in popular Nollywood texts. it deals extensively with popular perceptions of women and the location of the discourses of their representations in African cities. While there is nothing theoretically earth-shaking about the way Little’s book describes this space of narrative articulation, i invoke Little’s study in my reading of women in Nollywood films for other reasons, the most obvious being my desire to show that the observations he made in the 1970s were as valid then as they are in 2012. my intention in this regard is to recontextualize Little’s premise to fit the context of Nollywood films. Constructing my reading of women as a historical continuum that begins with Little’s study in the 1970s allows me to shed light on the prevalence of the narrative marginality to which women are consigned in the urban African popular imagination, a narrative option to which Nollywood has so far subscribed in the three decades or so of its existence. i would suggest that this narrative option has remained part of Nollywood’s aesthetic diet because there is a carry-over of the aesthetic presence of women from the older art form of literature to the newer media-mediated images of Nollywood. this is the point that Adeleye-Fayemi makes eloquently in her essay “either One or the Other: Images of Women in Nigerian Television.” Nollywood filmmakers, many of whom came from television, did not abandon this preoccupation and the peculiar ways women are represented for and on television in Nigeria. the television audience, which Adeleye-Fayemi discusses in her essay, is no different from that for Nollywood. Like the television public that Adeleye-Fayemi studied, Nollywood’s public is “popular,” meaning that it is distinct from those that read the plays of Wole Soyinka or the novels of Chinua Achebe. karin Barber’s eloquent study of audience in Africa, “Preliminary Notes on the Audience in Africa,” offers valuable insight into understanding this group. She argues, for instance, that “the concept of the ‘public,’ then, as a new form of coming-together characteristic of the colonial era, is a powerful one, but one which must be carefully qualified and which can
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".