Measuring constitutional preferences: A new method for analyzing public consultation data.

Public consultation has become an indispensable part of constitutional design, yet the voluminous, narrative data produced are often impractical to analyze. There are also few, if any, standards for such analysis. Using a comprehensive reference ontology from the Comparative Constitutions Project (CCP), we develop a new methodology to identify constitutional topics of most concern to citizens and compare these to topics in constitutions globally. We analyze data from Chile's 2016 public consultations-an ambitious process that produced nearly 265,000 narrative responses and launched the constitutional reform process that remains underway today. We leverage advances in natural language processing, in particular sentence-level semantic similarity technology, to classify consultation responses with respect to constitutional topics. Our methodology has potential for advocates, drafters, and researchers seeking to analyze public consultation data that too often go unexamined.

Associative models fail to characterize transitive inference performance in rhesus monkeys (Macaca mulatta).

It has been suggested that non-verbal transitive inference (if A > B and B > C, then A > C) can be accounted for by associative models. However, little is known about the applicability of such models to primate data. In Experiment 1, we tested the fit of two associative models to primate data from both sequential training, in which the training pairs were presented in a backward order, and simultaneous training, in which all training pairs are presented intermixed from the beginning. We found that the models provided an equally poor fit for both sequential and simultaneous training presentations, contrary to the case with data from pigeons. The models were also unable to predict the robust symbolic distance effects characteristic of primate transitive choices. In Experiment 2, we used the models to fit a list-linking design in which two seven-item transitive lists were first trained independently (A > B…. > F > G and H > I …. > M > N) then combined via a linking pair (G+ H-) into a single, 14-item list. The model produced accurate predictions for between-list pairs, but did not predict transitive responses for within-list pairs from list 2. Overall, our results support research indicating that associative strength does not adequately account for the behavior of primates in transitive inference tasks. The results also suggest that transitive choices may result from different processes, or different weighting of multiple processes, across species.