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Record W4392757331 · doi:10.1080/14662043.2024.2324522

‘Thin’ loyalty and declining attachment to the African National Congress

2024· article· en· W4392757331 on OpenAlexfundno aff
Michael Braun

Bibliographic record

VenueCommonwealth and Comparative Politics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSouth African History and Culture
Canadian institutionsnot available
FundersJackman Humanities Institute, University of TorontoUniversity of Toronto
KeywordsLoyaltyVotingPoliticsGovernment (linguistics)Political scienceDemocracyPolitical economyPublic administrationAdvertisingPublic relationsEconomicsBusinessLaw

Abstract

fetched live from OpenAlex

The African National Congress (ANC) has been an electorally dominant party in South African politics since 1994, with its vote share peaking in 2004 before falling to a low in the most recent 2019 general elections. Simultaneously, there have been much sharper declines in levels of ANC partisanship and assessments of government performance among the party’s own voters. This presents a puzzle: Why do a significant share of ANC voters continue to support a party that they do not ‘feel close’ to and do not believe is adequately managing the economy or the delivery of public goods? Based upon original qualitative data from semi-structured interviews with 111 intended ANC voters, I argue that there is a sizeable portion of ANC voters whose connection to the party is characterised by a conditional loyalty that falls short of a more thoroughgoing partisanship. The persistence of ‘thin’ loyalty alongside habitual and strategic voting for the ANC – especially among the ‘born free’ generation – has obfuscated the extent of decline in the perceived efficacy of voting and overall satisfaction with the outcomes of democratic politics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.129
GPT teacher head0.410
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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