The connective or can be treated as an inclusive disjunction or else as an exclusive disjunction. Although researchers are aware of this distinction, few have examined the conditions under which each interpretation should be anticipated. Based on linguistic-pragmatic analyses, we assume that interpretations are initially inclusive before either (a) remaining so, or (b) becoming exclusive by way of an implicature (but not both). We point to a class of situations that ought to predispose disjunctions to inclusive interpretations and to situations that encourage exclusive interpretations. A disjunction's ultimate interpretation is based on its potential informativeness, where the interpretation of the disjunctive utterance having the smallest number of true conditions is considered most informative. Our investigation leads to five experiments employing arbitrary materials. Among the problems expected to encourage inclusive interpretations are those that present disjunctions in the antecedents of conditionals and in question forms. The best candidates to produce implicatures are those disjunctions that underdetermine an expected conjunctive conclusion, although other disjunctive utterances that are more informative as exclusive are discussed and tested.Requests for reprints should be sent to

Conçu pour les enseignant-es qui, intervenant dans l’enseignement supérieur ou dans la formation d’adultes, souhaitent élaborer de nouveaux enseignements ou en revisiter d’anciens, cet ouvrage propose une approche pratique, visuelle et modulaire fondée sur le principe de la cohérence pédagogique, et articulée autour d’un canevas intégrant les principales dimensions d’un enseignement. Proposant une démarche en trois temps, ce livre privilégie la réflexion individuelle à partir de bases théoriques solides pour permettre à chacun et chacune d’élaborer sa vision de l’enseignement, de construire ou de faire évoluer un enseignement, et de développer ses compétences pédagogiques.

Course design in higher education is often approached in a very linear and text-based manner. The paper presents a visual tool in the form of a canvas aimed at accompanying teachers in the design of courses. The canvas can be used in an individual or co-teaching setting. It can be applied either during the conception phase of a new course or to revisit and reflect an existing course.The visual dimension departs from the usual text-based format and ambitions to offer a practical and intuitive approach. It aims at engaging teachers to adopt a prototyping approach in the design of courses. It builds on the various visual modeling tools offered in the fields of business and strategy.The proposed canvas is part of a broader project accompanying higher education teachers in the clarification of their pedagogical intent, in ensuring constructive alignment and in the adoption of a reflexive posture on their teaching experiences.

Background: Unplanned hospital readmissions are a major healthcare and economic burden. This study compared statistical methods and machine learning algorithms for predicting the risk of all-cause 30-day hospital readmission in two French academic hospitals.Methods: The dataset included hospital stays selected from the clinical data warehouses (CDW) of the two hospitals (Rennes and Tours Academic Hospitals) using the criteria of the French national methodology to measure the 30-day readmission rate (i.e. ≥18-year-old patients, geolocation, no iterative stays, and no hospitalization for palliative care). Then, the prediction performance of Logistic Regression, Naive Bayes, Gradient Boosting, Random Forest, and Neural Networks were compared separately for the two hospitals but using the same CDW data pre-processing for all algorithms. The area under the receiver operating characteristic curve (AUC) was calculated for the 30-day readmission prediction performance of each model as well as the time to train the algorithm.Results: In total, 259,092 and 197,815 stays were included from the Rennes and Tours Academic Hospital CDWs, respectively, with readmission rates of 8.8% (Rennes) and 9.5% (Tours). The AUC of the regression models for the two hospitals ranged from 0.61 to 0.64, with computation times exceeding 18 hours. The AUC of the machine learning models ranged from 0.61 to 0.69 with computation times below 13 hours.Conclusions: Better performance and shorter computation times are obtained with machine learning methods. It is still necessary to compare different algorithms to identify the most efficient model.

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