Towards Gender Equality a Comparative Analysis of Gender Attitudes in Africa
Bibliographic record
Abstract
Gender attitudes and its factors continue to be debated in an era where gender equality remains a priority to countries in the world. Modernization theorists have assumed a predictable positive pattern of the influence of economic development on gender attitudes, thereby arguing that higher levels of economic development such as GDP per Capita, increases support for gender equality across countries. Whereas this has been proven in European and Western countries, it is difficult to generalize the results to African countries as the phenomenon is understudied on the continent. Using data from the 5th round of the Afrobarometer survey and multiple regression/multi-level analysis, this study investigated the influence of economic development, in addition to other socio-cultural factors, on gender attitudes in 34 African countries. The study revealed that a) among countries in Africa, economic development, in this case GDP per Capita, does not significantly influence attitudes towards gender equality; b) people’s ethnic background influences their attitudes towards gender equality; and c) Gender attitudes are strongly influenced by education, employment status and religious denomination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".