Audience and Aetonormativity in Picturebook to Film Adaptations

Chapter four suggests that the polarization of adult/child binaries in picturebook adaptations consistently highlights adult roles and presence within the story more than in the source, often foregrounding adult characters and featuring adults learning lessons from children. The chapter uses The Lorax and Jumanji to reveal how dual audience works differently in picturebooks and film, highlighting how these films seem to overturn adult/child binaries, placing children in increased power positions for a time, but eventually reestablish aetonormative power structures. The chapter ends by examining Spike Jonze’s controversial adaptation of Maurice Sendak’s Where the Wild Things Are, a film that emphasizes a common ideology that results from binary polarization in picturebook adaptation, wherein adults are portrayed as feeling powerless despite their seeming position of power.

The Once-ler Problem: Introduction to Decision Trees

In the “Once-ler Problem,” the decision tree is introduced as a very useful technique that can be used to answer a variety of questions and assist in making decisions. This chapter builds on the “Lorax Problem” introduced in Chapter 19, where Bayesian networks were introduced. A decision tree is a graphical representation of the alternatives in a decision. It is closely related to Bayesian networks except that the decision problem takes the shape of a tree instead. The tree itself consists of decision nodes, chance nodes, and end nodes, which provide an outcome. In a decision tree, probabilities associated with chance nodes are conditional probabilities, which Bayes’ Theorem can be used to estimate or update. The calculation of expected values (or expected utility) of competing alternative decisions is provided on a step-by-step basis with an example from The Lorax.

The Lorax Problem: Introduction to Bayesian Networks

The “Lorax Problem” introduces Bayesian networks, another set of methods that makes use of Bayes’ Theorem. The ideas are first explained in terms of a small, standard example that explores two alternative hypotheses for why the grass is wet: the sprinkler is on versus it is raining. The chapter describes how to depict causal models graphically with the use of influence diagrams and directed acyclic graphs. Bayes’ Theorem is used to compute conditional probabilities and to update probabilities once new information is obtained or assumed. The software program Netica is introduced. Finally, the chapter provides a second example of Bayesian networks based on The Lorax by Dr. Seuss. The reader will gain a firm understanding of parent nodes (also known as root nodes), child nodes, conditional probability tables (CPTs), and the chain rule for joint probability.