Is Culture the Cause? Choices, Expectations, and Electoral Politics in Solomon Islands and Papua New Guinea
Bibliographic record
Abstract
Research on Solomon Islands and Papua New Guinea typically offers one of two explanations for the choices voters make, and the way these choices contribute to those countries' poor political governance. The first explanation focuses on culture's influence on the expectations that voters hold of politicians, contending that the Big Man style of local leadership traditionally found in both countries has shaped voter expectations in ways that cause voters to demand local or personal benefits from MPs rather than good national governance. The second explanation hinges on rational choice models of voter behaviour and does not include culture in its list of explanatory variables. In this paper I argue that neither explanation fits well with key features of these countries' politics. Drawing on quantitative and qualitative data I show that, while voters are broadly rational and can readily distinguish modern politics from traditional leadership, culture still matters. In particular, informal institutions, associated with the countries' cultural contexts, influence voter behaviour and electoral collective action, and through this political governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".