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Table 1

Four reranking rules for the stochastic EDRA

Reranking rulePromotion amountWhich constraints are demoted
(Gradual) Error-Driven Constraint Demotion (EDCD; Tesar and Smolensky 1998) p = 0 Only the undominated loser-preferrers 
Gradual Learning Algorithm (GLA; Boersma 1997, 1998) p = 1 All loser-preferrers 
Minimal Gradual Learning Algorithm (minGLA; Boersma 1997, 1998) p = 1 Only the highest loser-preferrer 
Calibrated Error-Driven Ranking Algorithm (CEDRA; Magri 2012) 
graphic
 
Only the undominated loser-preferrers 
Reranking rulePromotion amountWhich constraints are demoted
(Gradual) Error-Driven Constraint Demotion (EDCD; Tesar and Smolensky 1998) p = 0 Only the undominated loser-preferrers 
Gradual Learning Algorithm (GLA; Boersma 1997, 1998) p = 1 All loser-preferrers 
Minimal Gradual Learning Algorithm (minGLA; Boersma 1997, 1998) p = 1 Only the highest loser-preferrer 
Calibrated Error-Driven Ranking Algorithm (CEDRA; Magri 2012) 
graphic
 
Only the undominated loser-preferrers 
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