Beyond Fear: How Everyday Interactions Defuse Xenophobia in South African Townships
Bibliographic record
Abstract
Scholarship and research following the onset of the widespread 2008 xenophobic attacks in South Africa have largely been negative, presenting a picture of perpetual hatred, communities falling into abysmal fear, livelihoods and lives being lost, and the relative peace and harmony enjoyed by locals and foreigners since South Africa gained its independence in 1994 being lost forever. Such rhetoric has been entrenched by the continued rise in cases of civil unrest, perpetuated by individuals and groups who champion selfish interests at the expense of foreigners. Yet, naturally, society has shown the ability to transform negative experiences into positive outcomes, characterized by deeper social ties, co-existence, and mutually beneficial oneness in diversity. Based on ethnographic research conducted to examine everyday social interaction among migrants from the Great Lakes Region and South Africans in Cape Town, this paper calls for a re-examination of the roots and results of xenophobia, showing, through the examples of Joe Slovo and Phoenix Townships in Cape Town, how positive social movements can be borne out of negative situations. The paper argues, with evidence from the two townships, that what may have changed since 2008 is the nature of the social fabric that is interaction-based, that which holds a people either together or asunder, and not their economic outlook. The paper delves into the controversy surrounding the social menace of xenophobia, which, although widely written about, none have been wholesomely able to account for. Perhaps the answer to why there has been this ugly scar on South Africa lies in the examination not of what has been, but in areas seemingly unimportant. The question is: why has xenophobia been continuously experienced in some areas more than others, yet poverty is as endemic and perpetual an element in South African townships? Part of the answer lies in the examination of social relations between African migrants and locals in areas least hit by the social cancer. Based on evidence, xenophobia in South Africa is argued to be more a social than an economic consequence, whose trajectory is changeable once the right social tools are employed. These positive results can also serve as models for social reconstruction if a sustainable solution to social ills such as xenophobia can be found.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".