Multimembership and Identity in Writing Centers and in Industry: Examining the Work of Reconciliation in the Michigan Tech Multiliteracies Center and at Kimberly-Clark Corporation

Writing center scholarship and practice have approached how issues of identity influence communication but have not fully considered ways of making identity a key feature of writing center research or practice. This dissertation suggests a new way to view identity -- through an experience of "multimembership" or the consideration that each identity is constructed based on the numerous community memberships that make up that identity. Etienne Wenger (1998) proposes that a fully formed identity is ultimately impossible, but it is through the work of reconciling memberships that important individual and community transformations can occur. Since Wenger also argues that reconciliation "is the most significant challenge" for those moving into new communities of practice (or, "engage in a process of collective learning in a shared domain of human endeavor" (4)), yet this challenge often remains tacit, this dissertation examines and makes explicit how this important work is done at two different research sites - a university writing center (the Michigan Tech Multiliteracies Center) and at a multinational corporation (Kimberly-Clark Corporation). Drawing extensively on qualitative ethnographic methods including interview transcriptions, observations, and case studies, as well as work from scholars in writing center studies (Grimm, Denney, Severino), literacy studies (New London Group, Street, Gee), composition (Horner and Trimbur, Canagarajah, Lu), rhetoric (Crowley), and identity studies (Anzaldua, Pratt), I argue that, based on evidence from the two sites, writing centers need to educate tutors to not only take identity into consideration, but to also make individuals' reconciliation work more visible, as it will continue once students and tutors leave the university. Further, as my research at the Michigan Tech Multiliteracies Center and Kimberly-Clark will show, communities can (and should) change their practices in ways that account for reconciliation work as identity, communication, and learning are inextricably bound up with one another.

Automatic Food Intake Assessment Using Camera Phones

Obesity is becoming an epidemic phenomenon in most developed countries. The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. It is essential to monitor everyday food intake for obesity prevention and management. Existing dietary assessment methods usually require manually recording and recall of food types and portions. Accuracy of the results largely relies on many uncertain factors such as user's memory, food knowledge, and portion estimations. As a result, the accuracy is often compromised. Accurate and convenient dietary assessment methods are still blank and needed in both population and research societies. In this thesis, an automatic food intake assessment method using cameras, inertial measurement units (IMUs) on smart phones was developed to help people foster a healthy life style. With this method, users use their smart phones before and after a meal to capture images or videos around the meal. The smart phone will recognize food items and calculate the volume of the food consumed and provide the results to users. The technical objective is to explore the feasibility of image based food recognition and image based volume estimation. This thesis comprises five publications that address four specific goals of this work: (1) to develop a prototype system with existing methods to review the literature methods, find their drawbacks and explore the feasibility to develop novel methods; (2) based on the prototype system, to investigate new food classification methods to improve the recognition accuracy to a field application level; (3) to design indexing methods for large-scale image database to facilitate the development of new food image recognition and retrieval algorithms; (4) to develop novel convenient and accurate food volume estimation methods using only smart phones with cameras and IMUs. A prototype system was implemented to review existing methods. Image feature detector and descriptor were developed and a nearest neighbor classifier were implemented to classify food items. A reedit card marker method was introduced for metric scale 3D reconstruction and volume calculation. To increase recognition accuracy, novel multi-view food recognition algorithms were developed to recognize regular shape food items. To further increase the accuracy and make the algorithm applicable to arbitrary food items, new food features, new classifiers were designed. The efficiency of the algorithm was increased by means of developing novel image indexing method in large-scale image database. Finally, the volume calculation was enhanced through reducing the marker and introducing IMUs. Sensor fusion technique to combine measurements from cameras and IMUs were explored to infer the metric scale of the 3D model as well as reduce noises from these sensors.