Trust is personal <i>and</i> professional: The role of trust in the rise and fall of a South African civil society coalition
Bibliographic record
Abstract
This article explores trust dynamics among a coalition of civil society organizations called Unite Behind that formed in Cape Town, South Africa, in late 2017. Unite Behind was established to demand more accountability from a state marred by corruption—and specifically for the resignation of then President Jacob Zuma. When Zuma resigned, the coalition attempted to transition to a social movement campaigning for social justice but declined as a coalition into an organization of sorts. Taking trust as a positive belief in the reliability, truth or ability of an actor or entity, this article argues that conceptions of political and social/generalized trust are of less importance in explaining the rise and fall of Unite Behind than a combination of personal trust in particular leaders, and a form of particularized trust, namely, trust in other organizations. This notion of organizational trust as a form of particularized trust is of potential wider importance to the analysis of civil society network co-ordination.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".