Rotten Tomatoes Research Articles

Queer Intimacy: Vocality in Jesus Christ Superstar

Queer IntimacyVocality in Jesus Christ Superstar Larissa A. Irizarry (bio) Andrew Lloyd Webber and Tim Rice’s Jesus Christ Superstar is a rock opera depicting the last week of Jesus Christ’s life from the perspective of Judas Iscariot.1 Norman Jewison’s 1973 adaptation of the rock opera into a musical film (hereafter JCS) is important to both musical film studies and the growing discourse of queer musicology because, I argue, the characters’ embodiment of authenticity through fraught vocality destabilizes the passion narrative not only by presenting Judas (Carl Anderson) as a tragic hero but also by depicting his relationship with Jesus (Ted Neeley) as embodying queer intimacy that is inflected by race, with a black Judas opposite a white Jesus.2 To read Judas and Jesus’s relationship as queer is a departure from the film’s reception history and from contemporary representations of the Jesus figure in popular culture, which have strongly aligned him with heterosexual kinship structures.3 The film’s premiere was met with an overall positive popular reception.4 Reviews from critics, on the other hand, were mixed, and the film’s queer framing of erotics went largely undetected. Gary Arnold of the Washington Post said of the musical numbers that, “after coming on strenuously, many of the numbers seem to end with a shocking lack of either point or ceremony.” He further labeled the film as nothing more than a kitschy commercialization of the passion story. Arthur D. Murphy of Variety disliked the anachronistic use of such props as war tanks, claiming [End Page 162] that the drama depended on “forced metaphor to [the] outright synthetic.”5 Gene Siskel of the Chicago Tribune disliked the actors’ characterization of the lead roles, calling them “so confused, so shapeless, [that] the film cannot succeed in any meaningful way.”6 Rotten Tomatoes, a review-aggregation website, gave the film a 55 percent rating, with the critique that “the miscasting and tonal monotony halts this musical’s groove.”7 US-based Jewish organizations gave the most condemning reviews, labeling the film anti-Semitic in its portrayal of a group of Jews as the most villainous characters (a prime example being the musical number “This Jesus Must Die,” sung not by the Romans who killed Jesus but by the Sanhedrin). This claim escalated to the point where the Israeli government issued a statement distancing itself from the film, which was shot on location in Israel.8 The film was labeled by some commentators not only as anti-Semitic but as antiblack and imperialistic through reference to a black Judas and an Asian American Mary Magdalene (Yvonne Elliman).9 JCS was also considered provocative in its portrayal of intimacy between Jesus and Mary Magdalene, which characterized Jesus as a sexually integrated person rather than an asexual, transcendent messiah.10 The discourse concerning Jesus’s intimate relations is in line with other works (artistic and otherwise) that assume Jesus’s heterosexuality. In the gnostic Gospel of Philip, for example, Mary is called the most loved companion of Jesus, whom he frequently kisses on the mouth.11 Other modern films have explored this theme, including The Last Temptation of Christ (1988), whose plot is based on Jesus being tempted to eschew his messianic duties and instead start a family with Mary.12 In Stephenson Humphries-Brooks’s study on cinematic depictions of Jesus, the Mary of JCS is understood as desiring to be Jesus’s lover.13 This contention is supported by the torch ballad “I Don’t Know How to Love Him,” sung by Mary. With Mary labeled the primary love interest, Jesus is firmly fixed to heteronormative scripts. Despite this traditionally heteronormative reading of JCS, the gendered performance of the two leading high tenors of the musical film—in combination with their shared musical material, parallel virtuosic vocal failures, and tessitural [End Page 163] intimacy—frustrates straight readings. Throughout the work, the actors suggest through their body language that Judas and Jesus know each other’s fears and weaknesses, more so than any other couple in the story. For instance, when Simon the Zealot suggests taking Rome by force, Judas and Jesus share a knowing glance of...

Jaws: the revenge and the production of failure

ABSTRACTMost studies of ‘so bad it’s good’ (SoBIG) and bad movies have understandably focused on issues of reception and especially fan consumption. This article, on the notorious 1987 disaster, Jaws: The Revenge, considers instead the production context of ‘badness’ and asks how a team of seasoned and talented Hollywood professionals managed to create a film with a 0% Rotten Tomatoes score. Unlike many ‘badfilms’, Jaws: The Revenge is a mainstream mid-budget film and neither a weird anomaly with a passionate and visible fan-base nor the product of an archaic cash-strapped production context. The article unpicks the film’s logic of production and argues that its badness was an emergent property of misguided creativity, deviations from audience expectations, a tight production schedule, and substandard but marginally competent execution. The filmmaking team were not idiots savants or (primarily) cynical exploitation merchants, but problem-solving professionals on a budget. Having tracked how the film’s SoBIG status was achieved within rather than outside Hollywood norms and conventions, the article concludes that the pedagogic value of Jaws: The Revenge is that its production of absolute failure offers a good case-study, paradoxically, of wider processes of industrial logic.

Investigating the Effects of Textual Reviews From Consumers and Critics on Movie Sales

In this study, we investigate the sales impact of different types of online word-of-mouth (WOM) based on their source (user versus critic) and form (structured versus unstructured). We propose a model by adopting the heuristic-systematic perspective and empirically test it using online movie reviews collected from Rotten Tomatoes, a review-aggregation website for film and television. We construct a unique dataset that matches critic reviews and user reviews, with metadata such as weekly box-office sales and weekly advertisement spending for 90 movies released in the U.S in 2014. In addition, we use a text-mining method to extract valuable sentiment information from the textual contents of both user and critic reviews. This dataset allows us to compare the box-office responsiveness of four types of reviews: user numeric ratings, user text reviews, critic numeric ratings, and critic text reviews. The results reveal that critic reviews and user reviews influence sales through different forms: while user reviews impact sales through their aggregate numeric ratings, critic reviews exert their impact through textual narratives. Our study provides managerial implications to businesses on how to allocate their resources on different social media-related marketing strategies to lift sales.

From Predictive to Prescriptive Analytics

We combine ideas from machine learning (ML) and operations research and management science (OR/MS) in developing a framework, along with specific methods, for using data to prescribe optimal decisions in OR/MS problems. In a departure from other work on data-driven optimization, we consider data consisting, not only of observations of quantities with direct effect on costs/revenues, such as demand or returns, but also predominantly of observations of associated auxiliary quantities. The main problem of interest is a conditional stochastic optimization problem, given imperfect observations, where the joint probability distributions that specify the problem are unknown. We demonstrate how our proposed methods are generally applicable to a wide range of decision problems and prove that they are computationally tractable and asymptotically optimal under mild conditions, even when data are not independent and identically distributed and for censored observations. We extend these to the case in which some decision variables, such as price, may affect uncertainty and their causal effects are unknown. We develop the coefficient of prescriptiveness P to measure the prescriptive content of data and the efficacy of a policy from an operations perspective. We demonstrate our approach in an inventory management problem faced by the distribution arm of a large media company, shipping 1 billion units yearly. We leverage both internal data and public data harvested from IMDb, Rotten Tomatoes, and Google to prescribe operational decisions that outperform baseline measures. Specifically, the data we collect, leveraged by our methods, account for an 88% improvement as measured by our coefficient of prescriptiveness. This paper was accepted by Noah Gans, optimization.

Patulin, ergosterol and Howard mold count (HMC): Which one is the best quality indicator for tomatoes?

Ergosterol, an important constituent of fungal cell wall, was started to be used as a quality parameter for the tomatoes and derivatives instead of Howard mold count (HMC). In addition, patulin is another important microbiological quality indicator of molds such as Penicillium, Aspergillus, and Byssochlamys. In this study, it is aimed to identify the relationship between HMC, patulin, and ergosterol in juices produced from the tomatoes which has the surface rotten of 0% (sound), 25%, 50%, 75%, and 100%. As the rotten proportion increased, HMC, ergosterol, and patulin values increased. Linear correlations (r) between HMC, ergosterol, patulin, and rotten proportion were calculated as 0.99, 0.99, and 0.98, respectively. In addition, linear correlations between HMC and ergosterol (r=0.95), HMC and patulin (r=0.95) and ergosterol and patulin (r=0.98) were found. In contrast, non-linear correlation between pH, brix and titratable acidity, and the rotten proportion was determined. PRACTICAL APPLICATIONS: The manuscript has provided the relationship between HMC, patulin, and ergosterol in juices produced from the tomatoes which have the surface rotten of 0% (sound), 25%, 50%, 75%, and 100%. Linear correlations between rotten proportions of tomatoes and HMC, ergosterol, patulin and between HMC and ergosterol, HMC and patulin, and ergosterol and patulin were determined in juices produced from rotten tomatoes. Patulin indicated correlation with HMC and ergosterol in juices produced from rotten tomatoes. In addition to HMC and ergosterol, patulin has great potential for the assessment of tomato products as associated with the rotten proportion.

Intelligent sentiment analysis approach using edge computing‐based deep learning technique

SummarySentiment analysis and opinion mining has become a major tool for collecting information from customer reviews on user sentiments and emotions, especially for online video streaming services and social networks. The increasing use of smartphones has popularized subscription to various streaming services that provide streaming media and video‐on‐demand. These applications offer a gateway to analyze user reviews by introducing sentiment analysis in the mobile environment. Online user reviews can hold a lot of useful information and help predict user interests. Analysis of user reviews can provide substantive information for business processing. Sentiment classification of these reviews is a commonly used analysis technique. Usually, these reviews are given in a text format, with every word in each considered a feature, so selection should focus on optimal features from all available features present in the reviews. This study employs machine learning algorithms to extract the best features from the training review data set. Then, the selected features are fed into the convolutional neural network and other fully connected layers for further processing. The proposed approach is evaluated with the standard evaluation metrics, such as precision, accuracy, recall, and f‐measure, using three distinct benchmark data sets: polarity, Rotten Tomatoes, and IMDb. This work has also employed a pretrained sentiment analysis model over an Android application framework to classify reviews on a Smartphone without the need for any cloud or server‐side API.

Opinion Mining: An Approach to Feature Engineering

Sentiment Analysis or opinion mining refers to a process of identifying and categorizing the subjective information in source materials using natural language processing (NLP), text analytics and statistical linguistics. The main purpose of opinion mining is to determine the writer’s attitude towards a particular topic under discussion. This is done by identifying a polarity of a particular text paragraph using different feature sets. Feature engineering in pre-processing phase plays a vital role in improving the performance of a classifier. In this paper we empirically evaluated various features weighting mechanisms against the well-established classification techniques for opinion mining, i.e. Naive Bayes-Multinomial for binary polarity cases and SVM-LIN for multiclass cases. In order to evaluates these classification techniques we use Rotten Tomatoes publically available movie reviews dataset for training the classifiers as this is widely used dataset by research community for the same purpose. The empirical experiment concludes that the feature set containing noun, verb, adverb and adjective lemmas with feature-frequency (FF) function perform better among all other feature settings with 84% and 85% correctly classified test instances for Naïve Bayes and SVM, respectively.

The effects of eWOM volume and valence on product sales – an empirical examination of the movie industry

The present research examines how quantitative and qualitative aspects – volume and valence – of eWOM on various social media platforms in the movie industry, along with other forms of marketing communications (e.g. advertising expenditure), predict box office and DVD sales for pre- and post-periods of movie releases. The analysis of aggregated big data from multiple sources – 65,665,859 social media postings from blogs, forums, news, and Twitter, Nielsen’s ad expenditure data, and Rotten Tomatoes ratings for 170 movies—revealed that the explanatory power of the model for box office revenue and DVD sales mostly comes from the volume, rather than valence, of eWOM.

Do consumer and expert reviews affect the length of time a film is kept on screens in the USA?

We evaluate the effect of critical reviews by consumers and experts on a film’s running time at movie theaters in the USA using survival regression analysis. In addition to the usual expert critics’ reviews, we employ the consumer reviews rating and their affectivity about films as proxies for the consumer influence effect. To provide measures for consumer affectivity, we perform affective computing using mining techniques of sentiment and emotion on consumer reviews. We build a very rich film dataset by collecting information from the Box Office Mojo and the Rotten Tomatoes sites, including all matched films released between 2004 and 2015 that are available on these sites. We find evidences of consumer ratings matter in keeping a film running longer at the theaters, but experts’ ratings have a larger influence on the movie market as a whole. Estimates by genre indicate that the influence of expert reviews on the length of run of widely opening film releases, which include blockbusters, is null, but that their influence on narrowly released films is large. Also, film running times of genres like foreign, drama and action films are greatly influenced by sentiments and emotions spread by consumers through their reviews.

Molecular characterization of Alternaria alternata population isolated from Upper Egyptian tomato fruits

AbstractAlternaria alternata is the most common fungal pathogen of tomatoes in Upper Egypt. Morphological identification of this fungus is challenging; therefore, this study searched for new classification tools based on molecular techniques. Using a dilution plating method, 67 strains of A. alternata were isolated from 34 samples of rotten tomato fruits representing the Giza 80 and Edkawy cultivars. The collected strains were identified using the amplification products of the internal transcribed spacer (ITS) region, glyceraldehyde 3‐phosphate dehydrogenase (Gpd) and Alt a1, which is a gene involved in the production of most of the allergens produced by A. alternata. The screening revealed that A. alternata constituted more than half of the total fungi recovered from rotten tomatoes in this study. According to the phylogenetic analysis using these three loci, the collected strains clustered in accordance with the host cultivar type from which they had been isolated. Specific gene random primer polymerase chain reaction (SGRP‐PCR) techniques indicated that the A. alternata population in the tested region has a high genetic diversity. The pathogenicity test showed that most of the A. alternata isolates (67.2%) were highly pathogenic, and no correlation was found between the phylogenetic analysis and pathogenicity. In addition, the influence of the fungicide Disan 80% on the collected strains showed significant differences that were attributed to the source of isolation.

Semi-Supervised Learning From Crowds Using Deep Generative Models

Although supervised learning requires a labeled dataset, obtaining labels from experts is generally expensive. For this reason, crowdsourcing services are attracting attention in the field of machine learning as a way to collect labels at relatively low cost. However, the labels obtained by crowdsourcing, i.e., from non-expert workers, are often noisy. A number of methods have thus been devised for inferring true labels, and several methods have been proposed for learning classifiers directly from crowdsourced labels, referred to as "learning from crowds." A more practical problem is learning from crowdsourced labeled data and unlabeled data, i.e., "semi-supervised learning from crowds." This paper presents a novel generative model of the labeling process in crowdsourcing. It leverages unlabeled data effectively by introducing latent features and a data distribution. Because the data distribution can be complicated, we use a deep neural network for the data distribution. Therefore, our model can be regarded as a kind of deep generative model. The problems caused by the intractability of latent variable posteriors is solved by introducing an inference model. The experiments show that it outperforms four existing models, including a baseline model, on the MNIST dataset with simulated workers and the Rotten Tomatoes movie review dataset with Amazon Mechanical Turk workers.

Posibilidades de lo audiovisual en las redes sociales

The internet is doing more than uniting people, communities or countries: it is shaping the way our minds think and conceive the passing of information. The result of this is an immediate communication, an impatient speaker and, above all, a message that aims to be interactive. Videos from Facebook and YouTube, Netflix, the trailers as major events and all their possibilities of viralization through a simple hashtag, or a good or bad rating in Rotten Tomatoes, are the new panorama in which the language of cinema travels.

Z: The Beginning of Everything

Z: The Beginning of Everything

Embedding unstructured side information in product recommendation

Abstract Various researchers have already engaged in using auxiliary side information within recommender applications to improve the quality and accuracy of recommendations. This side information has either been in the form of structured information such as product specifications and user demographic information or unstructured information such as product reviews. The abundance of unstructured information compared to structured information entices the use of such unstructured information in the recommendation process. Existing works that employ unstructured content have been confined to standard text modeling technique such as the use of frequency measures or topic modeling techniques. In this paper, we propose to model unstructured content about both products and users through the exploitation of word embedding techniques. More specifically, we propose to learn both user and product representations from any type of unstructured textual contents available in different external information sources using recurrent neural networks. We then apply our learnt product and user representations on two recommendation frameworks based on matrix factorization and link prediction to enhance the recommendation task. Experimental results on four datasets constructed from the Rotten Tomatoes website (movie review aggregator database) have shown the effectiveness of our proposed approach in different real-world situations compared to the state of the art.

FILM CRITICISM IN THE DIGITAL AGE

FILM CRITICISM IN THE DIGITAL AGE Mattias Frey and Cecilia Sayad, eds. New Brunswick: Rutgers UP, 2015, 273 pp.If Roland Barthes's seminal 1968 pronouncement of the "Death of the Author" helped give birth to postmodern literary theory, it is our own digital age that has led to the crashing fade-out of the professional-and even semiprofessional-film critic. Over the last decade, we have read a litany of postmortems, ranging from the snide "good riddance" to elegies invoking the ghosts of Pauline Kael, James Agee, Andrew Sarris, and other pre-digital critical auteurs.Although there has been a flurry of online articles underscoring this seismic shift, Film Criticism in the Digital Age ambitiously bills itself as "the first book-length study of its kind" (13). To that end, coeditors Frey and Sayad assemble both academic and popular analyses on the dearth-perhaps death-of the working film critic, caught up and rubbed out in the brave new World Wide Web of bottomless blogs and 140-character tweets. Refusing a simplistic "thumbs-up/thumbs down" approach, this useful if fitful anthology merits several stars, at least because it has the audacity to deliver its content in the old-school, contemplative, and tactile platform of print.Frey's introduction provides a valuable recap of recent reports on the demise of film criticism-which, unfortunately, has not been exaggerated by those men (and a few women) who have remained standing since journalistic ink started running red in the 1990s. Frey cites the Salt Lake Tribune's Sean Means, who lists over fifty film critics purged from 2006 to 2010-including such stalwarts as David Ansen of Newsweek (itself now out of print) and Variety's Todd McCarthy. From these opening shots, Frey focuses in on the book's main debates: for example, the relationship between the critic and his or her audience; the effect the new media forms have on criticism itself and the critic as occupation; and perhaps most Orwellian, whether "evaluation" should be the purpose of film criticism any longer in an age of cultural fragmentation and relativism. (To the latter, we might also add the morphing of popular film and TV into cultish mass constructs-Hunger Games or Game of Thrones anyone?)Contributors to the book, from noted scholar Thomas Elsaesser to controversial critic Armond White, primarily discuss the anarchic state of Anglo-American film criticism; however, also included are several globally oriented studies that will be of less interest to US readers. Threading through many of the essays are thoughts on the deeper debate regarding the culture and practice of the Internet itself. With no editorial "gatekeepers" anymore, nor the necessity of a media infrastructure per se, now anyone truly can be a critic (or, for that matter, an anonymous, toxic "troll"). As a result, this hyper-democratized explosion of popular voices has not only leveled the critical playing field but largely paved it over, engendering an equivalence of sorts between onetime elite (and discretely exclusive) print publications and the amateur "citizen journalist" writer. An upstart reviewing site can appear in a Google search within spitting distance of, say, the New York Times or Variety (especially if it is addressing an offbeat film or topic), and popular aggregate film sites such as Rotten Tomatoes tend to squish all the quoted reviewers together in one visual and evaluative field. Recently, Rotten Tomatoes has even begun grafting in audience comments, mutating into even more of a weedy, Franken-site hybrid.In the shrinking big picture of professional journalism in the age of Craigslist and Facebook, film criticism is only a small, if more battered, back page. Although Frey and Sayad competently treat the narrow issues, they tread lightly around the elephant in the (chat) room: the "free" and uncontrolled nature of Internet content that makes most for-profit news, arts, and culture sites slavishly dependent on (generically derived) advertising revenue, which is only a trickle compared to the streams that once flowed in print's heyday. …

Make-Believing Animated Films Featuring Digital Humans: A Qualitative Inquiry Using Online Sources

A qualitative inquiry of reviews of films featuring digital humanlike characters was performed by sampling user comments from three online reviewer aggregator sites: the Internet Movie Database, Rotten Tomatoes and Metacritic. The films chosen for analysis were: Final Fantasy: The Spirits Within (dir. Hironobu Sakaguchi and Motonori Sakakibara, 2001), The Polar Express (dir. Robert Zemeckis, 2004), and Beowulf (dir. Robert Zemeckis, 2007), all produced using CGI animation, together with A Scanner Darkly (dir. Richard Linklater, 2006) whose visuals are depicted by rotoscoping using Bob Sabiston’s Rotoshop software. The authors’ analysis identified individual differences in the viewing experience, particularly in relation to the uncertain ontology of the humanlike characters created using CGI (CGI-Humans). They found examples of reviews indicating an inability to distinguish between real and CGI-Human actors, observations of characters transiently exhibiting realism before returning to their artifice, and of characters being viewed as eerie (analogous to the uncanny valley), thus illustrating a complex and dynamic response to this phenomenon. In some situations, character uncanniness was related to the presence of an atypical feature such as movement of the eyes. Whilst specifically for Beowulf, perceptions became more problematic when there was familiarity with the actor playing the CGI-Human character, with some reviewers describing difficulties in categorizing the character as either real or animated. CGI-Human performances were also characterized by a lack of, or inappropriate, social interaction. Online reviewers did not perceive characters depicted using Rotoshop (Rotoshop-Humans) as eerie; rotoscoping was found to preserve, and possibly enhance, the natural social interactions between actors recorded from the live-action film which was used as the source material for the animation. The authors’ inquiry also identified user motivations for viewing these films and the importance placed by reviewers on the form of display when viewing the CGI films. They situate their interpretation of these findings in relation to Walton’s make-believe theory ( Mimesis as Make-Believe: On the Foundations of the Representational Arts, 1990).

Dawn of Justice, Dawn of Hope

A Review of Batman v Superman: Dawn of Justice (2016) by Zack Snyder (Director) According to the reviews, the 2016 DC Comics film, Batman v Superman: Dawn of Justice, directed by Zack Snyder, was a disappointment. It was the eagerly anticipated first appearance of Batman and Superman together on the big screen and a box office success, grossing $166 million in North America and $422.5 million worldwide in its opening weekend, to become the fourth largest global opening of all time (Box Office Mojo, 2016). But ultimately, the film received mixed reviews from fans and critics alike, with fan-site Rotten Tomatoes giving it a 27 percent rating, signifying a bad film (Rotten Tomatoes, 2016), and Metacritic a metascore of 44 percent from 51 critics, indicating mixed or average reviews (Metacritic, 2016). For many, it failed to be the film they had hoped for (Kermode, 2016).

An Iterative Method for Calculating Robust Rating Scores

In this paper we introduce an iterative voting algorithm and then use it to obtain a rating method which is very robust against collusion attacks as well as random and biased raters. Unlike the previous iterative methods, our method is not based on comparing submitted evaluations to an approximation of the final rating scores, and it entirely decouples credibility assessment of the cast evaluations from the ranking itself. The convergence of our algorithm relies on the existence of a fixed point of a continuous mapping which is also a stationary point of a constrained optimization objective. We have implemented and tested our rating method using both simulated data as well as real world data. In particular, we have applied our method to movie evaluations obtained from MovieLens and compared our results with IMDb and Rotten Tomatoes movie rating sites. Not only are the ratings provided by our system very close to IMDb rating scores, but when we differ from the IMDb ratings, the direction of such differences is essentially always towards the ratings provided by the critics in Rotten Tomatoes. Our tests demonstrate high efficiency of our method, especially for very large online rating systems, for which trust management is both of the highest importance and one of the most challenging problems.

Forecasting Demand in Supply Chain Using Machine Learning Algorithms

Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks (ANNs) trained with an effective second order algorithm, and Support Vector Machines (SVMs) for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews (Rotten Tomatoes), and a movie oriented social network (Flixster). Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.

How Martha Saved her Parents from Green Beans by D. LaRochelle

LaRochelle, David. How Martha Saved her Parents from Green Beans. Illus. Mark Fearing. New York: Dial Books for Young Readers, 2013. Print.This imaginative picture book is a tale that will delight picky eaters and those who live with them. Written in LaRochelle’s signature dramatic hyperbole, this 2014 Minnesota Book Award finalist puts a humorous and absurd twist on the classic ‘eat your vegetables’ dinner time conflict.Despite parental encouragement to eat her beans, Martha knows, “Green beans are bad. Very bad.” She is proved correct when a rogue band of mean green beans swagger into town. They chase old ladies, throw rotten tomatoes, make rude noises and kidnap Martha’s parents. Initially Martha is thrilled. She throws her plate of cold green beans out the window and settles in for a late night of cookies and movies. By morning, however, she misses her parents. When Martha discovers them tied up and surrounded by laughing, dancing villainous beans, she is forced to confront her worst nightmare: Is she “too much of a coward to eat a green bean”?This story is hilariously illustrated by Mark Fearing. The expressive faces of Martha, her parents, those dreaded green beans and even Martha’s dog provide eloquent parallels to the text. While parents may appreciate a conclusion in which the family enjoys alternative vegetables, vegetable haters everywhere will appreciate LaRochelle’s provocative conclusion: “Everyone knows that there is nothing bad about a nice leafy salad.” This boldly illustrated adventure will make an entertaining addition to any public, school or personal library serving young children.Highly Recommended: 4 out of 4 starsReviewer: Shelagh K. GenuisShelagh K. Genuis is an Alberta Innovates–Health Solutions Postdoctoral Research Fellow at the University of Alberta’s School of Public Health. Although an avid reader of biography, she has never stopped reading children’s fiction