The Complexities of the Internet as a Tool for Development: The Case of Pornography in South Africa.
Bibliographic record
Abstract
My thesis (DEVS 502) critiques ``practitioners of development'' for promoting the Internet as a development tool in Africa while ignoring the issue of pornography. It demonstrates that theincreased availability and exposure to pornography in Africa is likely to adversely affect the lives of both men and women. Pornography fails to promote safer sex to a population at significant risk of HIV/AIDS. It perpetuates constrictive notions of femininity and masculinity, portraying men as conquering the submissive women, while making male satisfaction the only important outcome of sexual acts. Pornography is not only a gender issue, but also a racial issue: it promotes “whiteness” by idealizing the white female body and portraying the limited number of black actors in a brutal, animal-like way. Pornography, thus, is a powerful medium that may prevent a necessary re-socialization among youth in South Africa to combat the ills of racism and gender inequality. Finally, pornography reflects the ways in which the West has historically held the power to influence gender, racial and sexual norms and values. In this sense, pornography can be viewed as another instance of Western colonialism. Through the lens of South Africa I show that the Internet will have negative consequences that need to be taken into account if it is applied as a development tool.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".