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Record W4401394275 · doi:10.37284/eajass.7.1.2088

Female Filmmakers in Nigeria: Breaking Barriers and Shaping Narratives

2024· article· en· W4401394275 on OpenAlexaff
Summer Okibe

Bibliographic record

VenueEast African Journal of Arts and Social Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsIndigenousMovie theaterNarrativeFilm industryGender studiesFace (sociological concept)Context (archaeology)Thematic analysisMedia studiesSociologyRepresentation (politics)Political scienceVisual artsHistorySocial scienceArtQualitative researchPoliticsLawLiterature

Abstract

fetched live from OpenAlex

This paper explores the significant contributions of Indigenous women filmmakers in Nigeria, highlighting their roles in breaking barriers and shaping narratives within the Nigerian film industry, known as Nollywood. Indigenous women have made remarkable strides in an industry traditionally dominated by men, using their platforms to challenge societal norms and advocate for gender equality. This paper discusses the historical context of Indigenous women in Nigerian cinema, profiling key figures such as Genevieve Nnaji, Kemi Adetiba, Mildred Okwo, and several others. Additionally, it examines the cultural and feminist themes prevalent in their work, the impact of their films, and the challenges they face. Special attention is given to the emerging trend of female filmmakers leveraging platforms like YouTube to produce and distribute their content. Through a detailed analysis of films such as "Lionheart," "King of Boys," and others, this paper illustrates how Indigenous women in Nigerian cinema reshape narratives and contribute to a more inclusive and diverse representation of African experiences. Including visual materials from the films discussed will enhance the understanding of the cultural and thematic elements these filmmakers bring to the screen. This paper aims to underscore the importance of supporting Indigenous women filmmakers and promoting gender equality within the film industry

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.311
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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