Bibliographic record
Abstract
The responses to the January 2015 looting of foreign-owned shops inSoweto and in April in Durban's central business district and elsewhere reveal more about the South African national consciousness than the events themselves. The ritual condemnations; the initial denial of xenophobia in preference to labelling it criminality; blaming victims and convoluted excuses of perpetrators are almost worse than the official silence and long-standing passivity about well-known xenophobic attitudes. When the President insists that "South Africans in general are not xenophobic", he ignores all surveys (Afrobarometer) showing a vast majority distrust (black) foreigners, wish to restrict their residence rights and prohibit the eventual acquisition of citizenship.On these scores South African attitudes are not unique. Antiimmigrant hostility inflicts most European societies. Perhaps suspicion of strangers is even universal: preferential kin selection as an evolutionaryadvantage, as sociobiologists assert. What is uniquely South African is the ferocious mob violence against fellow Africans. Why? The structural violence of apartheid laws has continued in the post-apartheid era for many reasons: the breakdown of family cohesion in poor areas which no longer shames brutalised youngsters; loss of moral legitimacy by government institutions, particularly a dysfunctional justice system; violence was glorified in the 'armed struggle', but, above all, marginalised slum dwellers learned that they only receive attention when they act destructively. Despite a rule bound constitution for conflict resolution, in a representative survey (Afrobarometer) 43 per cent in the Western Cape agreed with the suggestion that "it is sometimes necessary to use violence in support for a just cause".
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.037 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".