‘We vote for good things’: The moral economy of voting in South Africa, 2009-2019
Bibliographic record
Abstract
South Africa has witnessed the emergence of electoral competitiveness between 2009-2019 as the dominance of the African National Congress (ANC) began to decline. During this time there was an increase in both abstention and opposition party support, making it a fruitful time to interrogate how voters make the decisions which ultimately determine the trajectory of democracy - supporting the ruling party, abstaining, or voting for the opposition. In contrast to the dominant explanations of ‘rational’ economic voting in political science and neopatrimonialism in the African context, this study finds that voters evaluate their options based upon both strategic and affective considerations. Voters assess the likelihood that political parties will provide them with material benefits but are also influenced by party loyalty and perceptions about the credibility of parties in promoting the ‘just’ distribution of public goods. Amid declining ANC loyalty and corruption scandals, abstention was the dominant trend among voters who did not see any opposition parties as viable alternatives. Both voters and local activists wanted to support parties which showed a commitment to do ‘good things’ in their policy-orientations and actions, so opposition parties were most successful among young people when they appeared to be a credible and efficacious voice for the ideas and frustrations of their generation. The Economic Freedom Fighters (EFF) gained members and supporters with its populist political style, purporting to represent the interests of Black youth, mineworkers, and informal settlements. This strategy of opposition party building was effective where it could establish ideological and affective linkages among local activists who, in turn, could enhance the party’s reputation for contributing to the public ‘good’. In a context of vast inequality, urbanization, and single-party dominance, appropriating local struggles was a cost-effective strategy of opposition party building because it gave them a presence in the communities they claim to represent. Political ideas were packaged with a political style that projected efficacy, helping to recruit young people and build a partisan identity for those who felt excluded from the economic policies and political career pathways controlled by the ANC.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".