Candidate entry in a non-partisan context: Evidence from Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Why do candidates enter an electoral contest? The Rational Model of Candidate Entry offers a parsimonious explanation focusing on the probability of victory, the benefit of holding office, as well as campaign costs. Quality challengers enter when there is a high probability of victory, while long-shot races attract amateurs. In most contexts, the presence of parties makes it difficult to disentangle candidate decisions from organizational recruitment strategies. To test the basic assumptions of the Rational Model of Candidate Entry, this Research Note examines candidate entry decisions in Indonesia's Regional Representative Council ( Dewan Perwakilan Daerah, DPD), the world's largest elected non-partisan assembly. An analysis of constituency-level candidate lists in all four DPD elections indicates that entry decisions are affected by the perceived probability of victory, with fewer candidates entering in constituencies with a more concentrated vote in the previous election. Potentially winnable DPD races attract a greater number of experienced challengers, partisan amateurs, and non-partisan amateurs. Only the number of non-partisan amateur candidates consistently correlates with socio-demographic variables, further underlining the importance of electoral context for ambitious, politically savvy elites. The findings affirm the broad applicability of the Rational Model and spotlight Indonesia's often-overlooked DPD as a venue of strategic behaviour.
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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 | 0.001 | 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 it