The Journal of Popular CultureVolume 43, Issue 3 p. 524-539 The Rhetoric of Celebrity Cookbooks CHRISTINE M. MITCHELL, CHRISTINE M. MITCHELL Southeastern Louisiana UniversitySearch for more papers by this author CHRISTINE M. MITCHELL, CHRISTINE M. MITCHELL Southeastern Louisiana UniversitySearch for more papers by this author First published: 25 May 2010 https://doi.org/10.1111/j.1540-5931.2010.00756.xCitations: 16Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Works Cited Amazon. “Top Sellers List.”Amazon.com. 1996–2005. 10 Oct. 2005 〈http://www.amazon.com. Barnes and Noble. “Bestsellers: Daily Top 10 Books by Subject: Cooking, Food and Wine.”Barnes and Noble.com 1997–2005. 13 Oct. 2005 〈http://search.barnesandnoble.com/bestsellers/bestsellers.asp?CAT=914300&sort=S&userid=Nr4DHRGBLL. “Best Selling Cookbooks.”St. Petersburg Times Online 1 Sept. 2004. 1 Oct. 2005. 〈http://www.sptimes.com/2004/09/01/news_pf/Taste/Best_selling_cookbooks.shtml. “Bio: Bobby Flay.”Food Network.com. 2005. 9 Oct. 2005 〈http://www.foodnetwork.com/food/bobby_flay/article/0,1974,FOOD_9787,00.html. “Bio: Emeril Lagasse.”Food Network.com. 2005. 9 Oct. 2005 〈http://www.foodnetwork.com/food/emeril_lagasse/article/0,1974,FOOD_9823_1770157,00.html. “Bobby Flay's Biography.”StarChefs.com: The Magazine for Culinary Insiders 1997–2005. 15 Oct. 2005 〈http://www.starchefs.com/chefs/BFlay/html/start.shtml. Brown, Corie. “Just a Gigantic Rumble in the Belly?”Los Angeles Times 7 July 2004: F1–F2. “Chef Emeril Lagasse's Biography.”StarChefs.com: The Magazine for Culinary Insiders 1995–2005. 15 Oct. 2005 〈http://www.starchefs.com/ELagasse/html/biography.shtml. “Chef Julia Child's Biography on StarChefs.”StarChefs.com: The Magazine for Culinary Insiders 1995–2005. 10 Oct. 2005 〈http://www.starchefs.com/JChild/html/biography.shtml. Child, Julia. The Way to Cook. New York: Knopf, 1989. Curry, Dale. “Sara to Share Secrets in N.O.” [New Orleans] Times-Picayune 18 Mar. 2004: F-1, F-4. Downey, Kevin. “Food Network: Setting a Bigger Table.”Media Life Magazine 15 Mar. 2005. 9 Oct. 2005 〈http://www.medialifemagazine.com/News2005/mar05/mar14/2_tues/news4tuesday.html. Flay, Bobby, and Joan Schwartz. Bobby Flay's Boy Meets Grill. New York: Hyperion, 1999. Ingle, Schuyler. “Bobby Flay's Boy Meets Grill: Editorial Reviews.”Amazon.com. 15 Oct. 2005 〈http://www.amazon.com/exec/obidos/tg/detail/-/0786864907/103-0491296-0267011?v=glance. Lagasse, Emeril with Marcelle Bienvenu. Louisiana Real and Rustic. New York: Morrow, 1996. Miller, Samantha, and Lisa Kay Greissinger. “Hot Hands.”People Weekly 13 July 1998: 119–21. Research Library. ProQuest. Sims Memorial Library, Southeastern Louisiana University, Hammond, LA. 15 Oct. 2005 〈http://www.proquest.com/. Moulton, Sara. Sara Moulton Cooks at Home. New York: Broadway, 2002. National Cable & Telecommunications Association. “Food Network Fact Sheet.”NCTA.com. n.d. 9 Oct. 2005 〈http://www.ncta.com/guidebook_pdfs/FoodNetwork.pdf. Ray, Rachael. 30-Minute Meals. New York: Lake Isle, 1998. “Sara Moulton: Biography.”Sara Moulton.com. 2005. 13 Oct. 2005 〈http://www.saramoulton.com/bio.htm. Simba Information. “Julia Child's Death Heats up Demand for Her Classic Cookbooks.”Book Publishing Report 6 Sept. 2004: 5. Business Source Premier. EBSCO. Sims Memorial Library, Southeastern Louisiana University, Hammond, LA. 9 Oct. 2005 〈http://search.epnet.com. Simba Information. “Simba Projects 5.1% Revenue Growth for Cookbooks in 2005.”Book Publishing Report 25 Apr. 2005: 4. Business Source Premier. EBSCO. Sims Memorial Library, Southeastern Louisiana University, Hammond, LA. 9 Oct. 2005 〈http://search.epnet.com. Citing Literature Volume43, Issue3June 2010Pages 524-539 ReferencesRelatedInformation

The patient is a 74-year-old woman who was found to be in atrial fibrillation and admitted to the hospital. During her hospital stay, a chest radiograph demonstrated an illdefined nodule in the liver. A CT scan revealed three liver lesions ranging from 2.1 to 3.3 cm, typical for hemangiomas, and a 2.7-cm well-defined cystic lesion in the body of the pancreas. The patient underwent upper gastrointestinal endoscopy with ultrasound-guided fine-needle aspiration (EUS-FNA). The aspirated material was used to make direct smears and the needle rinse was placed in CytoLyt for cell block preparation. The cyst fluid was sent for amylase and CEA levels. The slides of both cysts were hypocellular with rare mucinous epithelial cells (Fig. C-1). There were numerous binucleate organisms ranging in size from 9 to 20 lm with a central axostyle, morphologically consistent with the trophozoites of G. lamblia (Fig. C-2). No organisms or mucinous epithelial cells were identified in the cell block. The pancreatic cyst fluid had an amylase level of 100 U/l and a CEA level of 37,749 ng/ml. These values supported the diagnosis of mucinous neoplasm. The patient underwent a distal pancreatectomy that showed a mucinous cystic neoplasm. No organisms were identified in the pancreatic cysts; however, the patient had received 3 days of treatment with metronidazole prior to surgery.

We propose the tutor/aid paradigm which integrates issues in intelligent technical training and real-time aiding of operators in complex dynamic systems. We focus on the pedagogical module of a tutor that not only compensates for a novice operator's deficiencies in knowledge and skills, but also prepares the operator to use the tutor as an assistant after training. As a first step to illustrate the tutor/aid paradigm, we present GT-VITA (Georgia Tech Visual Inspectable Tutor and Assistant), a proof-of concept ITS that has been successfully developed and evaluated, and is being fielded in the domain of satellite ground control.

Operations automation is a concept and design methodology for human-centered automation. Operations automation is automation that carries out, in whole or part, activities currently performed by operations personnel. Humans, however, remain essential components of the system, but perform a significantly different role. When autonomous systems reach their inevitable limits, operations personnel troubleshoot, diagnose, and repair the automation. This paper describes research exploring requirements for effective operations automation. First, it describes two field studies of conventional automation. Second, it proposes operations automation as an extension of the operator function model (OFM) and its computational implementation, OFMspert. The OFM is a normative behavioral model; OFMspert predicts and interprets operation actions. As such, the OFM and OFMspert offer potential computational architectures with which to implement operations automation. Finally, the paper describes an empirical study comparing conventional and operations automation. Results suggest operations automation dramatically enhances the ability of operations personnel to identify and diagnose failures.

Today, and for the foreseeable future, rapid change is a nearly ubiquitous characteristic of complex-dynamic systems. Widespread use of digital technology greatly increases the rate at which systems change and the complexity of systems. Changes in the work environment can degrade even the most skilled practitioner's expertise, creating gaps or misunderstandings in practitioner knowledge. Moreover, such gaps or misunderstanding can significantly affect performance. Under such circumstances, these practitioners, although highly skilled, can sometimes be thought of as trained novices. This research has two primary goals. The first goal is to address the growing training demands of maintaining practitioner expertise by using computer-based training that merges intelligent tutoring systems (ITS) and case-based teaching. The second goal is to implement this new approach in such a way that facilitates the ease and decreases the cost of incorporating new cases as training needs evolve. To address these goals, several components comprise this research. The key component is a conceptual architecture, the Case-Based Intelligent Tutoring System (CBITS). CBITS builds upon the experience and research in both ITS and case-based teaching. The ITS provides a control structure for monitoring individual students and addressing their individual needs. Within that structure, cases provide a method of teaching, using memorable experiences to create focused instruction. Cases also allow tutor content to evolve as the operational environment evolves. Another component of the research, the Georgia Tech Intelligent Tutoring Architecture (GT-ITACS), is a computational implementation of CBITS that separates training and domain knowledge from tutor software. GT-ITACS thus enables rapid and lower cost adoption of new cases into training. Training system implementations of CBITS are relevant in a range of domains in which practitioners interact with complex, technological, and evolving system. Examples include airline maintenance, electronic manufacturing, and telecommunications. In this research, CBITS is implemented as a proof-of-concept tutor for MD-11 pilots. The CBITS case in this implementation is a newly licensed capability of the MD-11 aircraft. This capability introduces a new technique that improves safety but is unfamiliar to experienced pilots. An empirical evaluation of the system with active airline pilots showed that the system provides significantly effective training.

This panel includes participants from academia and industry who have each made significant contributions to the design of effective visualizations to support decision making in a variety of domains. Panel members will offer an example of an innovative decision support visualization concept and discuss the underlying cognitive demands it is meant to support as well as any artifacts they used in its development. Both the nature of the visualization itself, and the linkage to the processes ‘behind the curtain’ will be presented. Panelists will discuss the various techniques used and the pro's and con's of each.

This paper describes a research program that addresses a variety of issues and problems in training pilots to fly modern, highly sophisticated aircraft First, the paper describes two intelligent tutors developed to teach two important aspects of aviation: a tutor that teaches management of the vertical dimension of the flight management system for pilots in transition training; and a tutor that teaches expert novices, that is, certified pilots, procedures not included in their transition training. Second, both tutors extend GT-ITACS (Georgia Tech-Intelligent Tutoring Architecture for Complex Systems), a computational development environment for intelligent tutors. In general, implementing instructional material in computational form is the most difficult step in developing effective computer-based instruction; this is particularly true for intelligent tutors Computational implementation is so difficult that implementing computer-based training is often cost and time prohibitive—explaining in part the small number of tutors and the abundance of computer-based training that is little more than Power Point presentations GT-ITACS is an computational architecture that is completely file driven That is, configuring a GT-ITACS tutor for a new application requires only reformatting input files. A tutor based on GT-ITACS does not require any additional software development or ‘hard-coding.’ Both tutors described in this paper instantiate GT-ITACS, demonstrating the versatility and power of the development environment. Finally, the paper concludes by describing a method to implement distributed, computer-based training, that is, training any time, any where A description of an easily implemented method, evaluated with both tutors described in this paper, provides users access to training at the place and time of their choice.

Change characterizes complex-dynamic systems, including the practitioners' role and required knowledge. Almost all change in these systems requires training. Moreover, as change is expected to be ongoing, so too will training. GT-CBITS (Georgia Tech Case-Based Intelligent Tutoring System) is an architecture for an intelligent tutor designed to teach highly skilled practitioners in complex-dynamic systems. GT-CBITS uses a case-based teaching approach that allows the training curriculum to be easily extended. This paper discusses some of the capabilities of the tutor and presents an example implementation.

Many computer-based training systems present instruction linearly, with exactly one path through the system that each student must follow. Students have little control over the pace, content branching, or flow of instruction. Student modeling within intelligent tutoring systems addresses these issues by interpreting student behaviors, representing the student's knowledge, and providing personalized instructional content. However, there is disagreement over the necessary content and structure of student models and their general utility. This paper discusses the development of an intelligent tutoring architecture with two instantiations that use sophisticated student models. Similar tutors with scaled-down student models will be used to evaluate the effectiveness of the differing student modeling approaches.

This paper begins with a discussion of the perceived requirement for general psychological models to underpin the design of human interaction with complex systems. A proposal is made that general psychological models may not be necessary, if indeed possible to create, for many applications of interest. For the applications of interest, cognitive engineering or human-machine systems models, such as the operator function model (OFM), may suffice. Next, a brief overview of the current state of OFM and its computational implementation, OFMspert, is presented. It describes how OFMspert has been extended to support the design of two intelligent tutoring systems (ITS) for operational control of safety-critical systems. An intelligent tutoring system requires two models: an expert model and a student model. This paper describes how the OFM can serve as the basis of the expert model and the OFMspert architecture as the basis for the architecture of the tutor itself.

To address the problem of human error in safety-critical systems, the GT-COMET (Georgia Tech Consequence Modeling for Error Tolerance) architecture is proposed. This architecture provides task management assistance to enhance error tolerance by helping human operators detect and correct potential errors before they have serious operational consequences. GT-COMET extends the OFM/OFMspert methodology, which attempts to match detected operator actions with actions expected by a normative model. Mismatches between detected actions and model expectations represent potential errors, and GT-COMET uses the likely consequences of these potential errors to construct task management reminders so the operators can correct errors before the system is adversely affected. This paper presents an overview of the GT-COMET architecture and its proof-of-concept implementation for pilots of an MD-11 aircraft.

The paper presents a brief overview of the current implementation and evolution of the operator function model (OFM) (C.M. Mitchell, 1987) and OFMspert, its computational implementation. It describes the proposed extension of these models to design operations automation, that is, automation that controls a system in a manner similar to that of a human operator. It then describes two operations automation research projects: AutoPass and Apprentice. AutoPass, now concluded, is presented, together with the empirical evaluation and results. A description of Apprentice, still in progress, concludes the paper.

With modern technology, rapid change characterizes complex dynamic systems, including the practitioners' role and required knowledge. Training in safety-critical domains must occur in a timely manner and ensure that practitioners understand changing systems and procedures. Web-based training provides an excellent opportunity to address these issues because distribution of training can be timely. easily accessed, and cost-efficient. Few sophisticated training systems exist for practitioners of complex dynamic systems. GT-ITACS (Georgia Tech Intelligent Architecture for Complex Systems) was designed and implemented as an intelligent tutoring system (ITS) "shell" to support several on-going Georgia Tech ITS research efforts. Access to domain-specific education and training from a Web-based library may meet the needs of training programs by allowing easy access to ongoing education and training material.

This paper begins with a discussion of a cognitive engineering model, the operator function model (OFM), to guide design of artifacts for human interaction with complex systems. Such artifacts include operator aids, associates, and tutors. The paper presents an overview of the current implementation and evolution of the operator function model (OFM) and OFMspert, its computational implementation. It describes how OFMspert has been extended to support the design of two intelligent tutoring systems (ITS) for operational control of safety-critical systems. Proof-of-concept demonstrations and evaluation teaching MD-11 transition pilots vertical navigation and a case-based tutor teaching currently certified MD-11 pilots new procedures, adapted since their certification training. 1. Background Almost two decades ago, Mitchell defined the operator function model (OFM) (Mitchell, 1987). The OFM is both a cognitive engineering and human-machine systems engineering model. It was created to provide a mathematical and visual representation of operator activities in control of complex, dynamic systems. The OFM makes explicit assumptions about modeling operator behavior in complex systems. These include hierarchy, heterarchy, and non-determinism. The OFM and its properties are extensively described in various publications (Mitchell, 1999; Thurman, Chappell, & Mitchell, 1998b). Inspection of Figure 1 shows a portion of an OFM implemented for aircraft navigation. Pieces of an OFM are often called OFM trees or subtrees. The OFM is a static model that represents when and how trees become active. The tree depicted in Figure 1 is active because the initiator, aircraft is not within limits of assigned heading, is true. The top-level activity, turn to assigned heading, decomposes into subtrees. Decompositions can take a variety of forms. Activities can be sequential (SEQ)—order is important. Others are heterogeneous (AND)—all activities must be performed but no order is required. Others are choices: an OR decomposition allows the operator to execute one or more activities; whereas hi an XOR decomposition activities are mutually exclusive and the operate must select exactly one. Figure 1 shows the decomposition for the activity, set FCP (flight control panel) heading target,' decomposed to the action level. Note that activity nodes are depicted as rounded-corner rectangles. Actions, the lowest level activity, are an exception. Actions are denoted by square-cornered rectangles. This syntax makes it easier to watch the run-time system. OFMspert is a computational and dynamic form of the OFM. OFMspert displays those portions of the OFM that are active at the present time. In 1986, the OFMspert project began. The Nii (Stanford) blackboard (Nii, 1986a; Nii, 1986b), an artificial intelligence methodology, offered an ideal software architecture with which to implement the OFM as a run-time system. The Nii blackboard is hierarchical, heterarchical, dynamic, and, in real time, processes incoming data to support current hypotheses, add new hypotheses, or reduce the likelihood of existing hypotheses. ACTIN (actions interpreter) is OFMspert's blackboard, displaying active trees and, in real time, connecting detected actions to expected actions. Linking detected actions to expected actions and using blackboard knowledge sources, specialized functions or methods, to ensure that the activity is proceeding correctly defines the intelligence that OFMspert brings to its applications. Failure to link an action to an existing activity or failure to detect an expected action indicates possible user errors. Figure 2 depicts a representation of the current OFMspert architecture. It is both domain and application independent. Domain-specific information, that is, a detailed description of the system of interest such as satellite control, aircraft navigation, or electronics manufacturing, is defined in files. Application-specific information is similar. Files define if the system will be an intelligent tutor, an operator's associate, or control automation. OFMspert reads these files at initialization and customizes the generic OFMspert (c)2000 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. to the domain and application of interest. Since the current version of OFMspert is implemented in standard Java, OFMspert is also platform independent. 2. Intelligent Tutoring Systems: Operational Training for Complex Systems In the workplace, training is mandatory. The rate of emerging, powerful, and inexpensive technology shows no signs of decreasing. Inevitably, some of this technology finds its way into most work domains. The rate of change in work domains due to the introduction of technology shows no sign of slowing. The introduction of new technology often fundamentally changes system operation (Woods, Johannesen, Cook, & Barter, 1994). Rapid change in the workplace means rapid change in the knowledge and operational skills to manage the changing systems. Thus, training becomes very important. Particularly for complex, often safety-critical, systems, effective and timely training, which keeps experienced operators abreast of how the system has changed and new methods for managing it, is mandatory to ensure system safety and efficiency. Legend push POP HDOnRK

Operations automation is automation that replaces, wholly or in part, operational activities currently carried out by human controllers in complex systems. It is intended to be neither ‘black-box’ nor ‘human-tended’ automation, but rather automation that functions independent of human control and yet still facilitates its inspection and repair as necessary. This paper describes research to develop an apprenticeship approach to developing a knowledge base to support such automation. The result is both a human-centered automation approach and a software architecture, Apprentice, to support this approach. Apprentice enables human operators to create the knowledge base for operations automation by performing their normal control activities. Apprentice watches and compares them with those specified in the knowledge base, noting discrepancies between the knowledge base and operator activities. Graphical knowledge base editing tools are then used—-by the domain practitioners—-to modify, refine, or extend the knowledge base as required to account for the detected discrepancies.

The VProf Tutor is a computer-based intelligent tutoring system (ITS) that teaches the use and understanding of vertical profile navigation modes to MD-11 pilots. This ITS is a proof-of-concept implementation of GT-ITACS (Georgia Tech-Intelligent Tutoring Architecture for Complex Systems). GT-ITACS is a domain- and platform- independent intelligent tutoring system shell designed to train operators of complex-dynamic systems. This paper introduces the features of GT-ITACS, using the VProf Tutor to illustrate the capabilities of the system.

Teams are often critical components of complex systems. Moreover, design of intelligent team training and aids depends on robust computational team models. To date, however, research has focused on computational models of individual decision-makers. No general computational team models exist. Models that do exist are typically quite primitive and restricted in domain of application. (Pew & Mayor, 1998) Thus, flexible, scalable, computational models of team decision making are urgently needed. In this paper, we describe a field study and proposed modeling extensions to the OFM/OFMspert methodology to represent team decision making. Specifically, this paper describes an initial extension of an OFM/OFMspert model for air traffic management (ATM), modeling the interaction between airline dispatchers and pilots-in-command of individual aircraft.