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1. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Author

Srivastava, Aarohi, Rastogi, Abhinav, Rao, Abhishek, Shoeb, Abu Awal Md, Abid, Abubakar, Fisch, Adam, Brown, Adam R., Santoro, Adam, Gupta, Aditya, Garriga-Alonso, Adrià, Kluska, Agnieszka, Lewkowycz, Aitor, Agarwal, Akshat, Power, Alethea, Ray, Alex, Warstadt, Alex, Kocurek, Alexander W., Safaya, Ali, Tazarv, Ali, Xiang, Alice, Parrish, Alicia, Nie, Allen, Hussain, Aman, Askell, Amanda, Dsouza, Amanda, Slone, Ambrose, Rahane, Ameet, Iyer, Anantharaman S., Andreassen, Anders, Madotto, Andrea, Santilli, Andrea, Stuhlmüller, Andreas, Dai, Andrew, La, Andrew, Lampinen, Andrew, Zou, Andy, Jiang, Angela, Chen, Angelica, Vuong, Anh, Gupta, Animesh, Gottardi, Anna, Norelli, Antonio, Venkatesh, Anu, Gholamidavoodi, Arash, Tabassum, Arfa, Menezes, Arul, Kirubarajan, Arun, Mullokandov, Asher, Sabharwal, Ashish, Herrick, Austin, Efrat, Avia, Erdem, Aykut, Karakaş, Ayla, Roberts, B. Ryan, Loe, Bao Sheng, Zoph, Barret, Bojanowski, Bartłomiej, Özyurt, Batuhan, Hedayatnia, Behnam, Neyshabur, Behnam, Inden, Benjamin, Stein, Benno, Ekmekci, Berk, Lin, Bill Yuchen, Howald, Blake, Orinion, Bryan, Diao, Cameron, Dour, Cameron, Stinson, Catherine, Argueta, Cedrick, Ramírez, César Ferri, Singh, Chandan, Rathkopf, Charles, Meng, Chenlin, Baral, Chitta, Wu, Chiyu, Callison-Burch, Chris, Waites, Chris, Voigt, Christian, Manning, Christopher D., Potts, Christopher, Ramirez, Cindy, Rivera, Clara E., Siro, Clemencia, Raffel, Colin, Ashcraft, Courtney, Garbacea, Cristina, Sileo, Damien, Garrette, Dan, Hendrycks, Dan, Kilman, Dan, Roth, Dan, Freeman, Daniel, Khashabi, Daniel, Levy, Daniel, González, Daniel Moseguí, Perszyk, Danielle, Hernandez, Danny, Chen, Danqi, Ippolito, Daphne, Gilboa, Dar, Dohan, David, Drakard, David, Jurgens, David, Datta, Debajyoti, Ganguli, Deep, Emelin, Denis, Kleyko, Denis, Yuret, Deniz, Chen, Derek, Tam, Derek, Hupkes, Dieuwke, Misra, Diganta, Buzan, Dilyar, Mollo, Dimitri Coelho, Yang, Diyi, Lee, Dong-Ho, Schrader, Dylan, Shutova, Ekaterina, Cubuk, Ekin Dogus, Segal, Elad, Hagerman, Eleanor, Barnes, Elizabeth, Donoway, Elizabeth, Pavlick, Ellie, Rodola, Emanuele, Lam, Emma, Chu, Eric, Tang, Eric, Erdem, Erkut, Chang, Ernie, Chi, Ethan A., Dyer, Ethan, Jerzak, Ethan, Kim, Ethan, Manyasi, Eunice Engefu, Zheltonozhskii, Evgenii, Xia, Fanyue, Siar, Fatemeh, Martínez-Plumed, Fernando, Happé, Francesca, Chollet, Francois, Rong, Frieda, Mishra, Gaurav, Winata, Genta Indra, de Melo, Gerard, Kruszewski, Germán, Parascandolo, Giambattista, Mariani, Giorgio, Wang, Gloria, Jaimovitch-López, Gonzalo, Betz, Gregor, Gur-Ari, Guy, Galijasevic, Hana, Kim, Hannah, Rashkin, Hannah, Hajishirzi, Hannaneh, Mehta, Harsh, Bogar, Hayden, Shevlin, Henry, Schütze, Hinrich, Yakura, Hiromu, Zhang, Hongming, Wong, Hugh Mee, Ng, Ian, Noble, Isaac, Jumelet, Jaap, Geissinger, Jack, Kernion, Jackson, Hilton, Jacob, Lee, Jaehoon, Fisac, Jaime Fernández, Simon, James B., Koppel, James, Zheng, James, Zou, James, Kocoń, Jan, Thompson, Jana, Wingfield, Janelle, Kaplan, Jared, Radom, Jarema, Sohl-Dickstein, Jascha, Phang, Jason, Wei, Jason, Yosinski, Jason, Novikova, Jekaterina, Bosscher, Jelle, Marsh, Jennifer, Kim, Jeremy, Taal, Jeroen, Engel, Jesse, Alabi, Jesujoba, Xu, Jiacheng, Song, Jiaming, Tang, Jillian, Waweru, Joan, Burden, John, Miller, John, Balis, John U., Batchelder, Jonathan, Berant, Jonathan, Frohberg, Jörg, Rozen, Jos, Hernandez-Orallo, Jose, Boudeman, Joseph, Guerr, Joseph, Jones, Joseph, Tenenbaum, Joshua B., Rule, Joshua S., Chua, Joyce, Kanclerz, Kamil, Livescu, Karen, Krauth, Karl, Gopalakrishnan, Karthik, Ignatyeva, Katerina, Markert, Katja, Dhole, Kaustubh D., Gimpel, Kevin, Omondi, Kevin, Mathewson, Kory, Chiafullo, Kristen, Shkaruta, Ksenia, Shridhar, Kumar, McDonell, Kyle, Richardson, Kyle, Reynolds, Laria, Gao, Leo, Zhang, Li, Dugan, Liam, Qin, Lianhui, Contreras-Ochando, Lidia, Morency, Louis-Philippe, Moschella, Luca, Lam, Lucas, Noble, Lucy, Schmidt, Ludwig, He, Luheng, Colón, Luis Oliveros, Metz, Luke, Şenel, Lütfi Kerem, Bosma, Maarten, Sap, Maarten, ter Hoeve, Maartje, Farooqi, Maheen, Faruqui, Manaal, Mazeika, Mantas, Baturan, Marco, Marelli, Marco, Maru, Marco, Quintana, Maria Jose Ramírez, Tolkiehn, Marie, Giulianelli, Mario, Lewis, Martha, Potthast, Martin, Leavitt, Matthew L., Hagen, Matthias, Schubert, Mátyás, Baitemirova, Medina Orduna, Arnaud, Melody, McElrath, Melvin, Yee, Michael A., Cohen, Michael, Gu, Michael, Ivanitskiy, Michael, Starritt, Michael, Strube, Michael, Swędrowski, Michał, Bevilacqua, Michele, Yasunaga, Michihiro, Kale, Mihir, Cain, Mike, Xu, Mimee, Suzgun, Mirac, Walker, Mitch, Tiwari, Mo, Bansal, Mohit, Aminnaseri, Moin, Geva, Mor, Gheini, Mozhdeh, T, Mukund Varma, Peng, Nanyun, Chi, Nathan A., Lee, Nayeon, Krakover, Neta Gur-Ari, Cameron, Nicholas, Roberts, Nicholas, Doiron, Nick, Martinez, Nicole, Nangia, Nikita, Deckers, Niklas, Muennighoff, Niklas, Keskar, Nitish Shirish, Iyer, Niveditha S., Constant, Noah, Fiedel, Noah, Wen, Nuan, Zhang, Oliver, Agha, Omar, Elbaghdadi, Omar, Levy, Omer, Evans, Owain, Casares, Pablo Antonio Moreno, Doshi, Parth, Fung, Pascale, Liang, Paul Pu, Vicol, Paul, Alipoormolabashi, Pegah, Liao, Peiyuan, Liang, Percy, Chang, Peter, Eckersley, Peter, Htut, Phu Mon, Hwang, Pinyu, Miłkowski, Piotr, Patil, Piyush, Pezeshkpour, Pouya, Oli, Priti, Mei, Qiaozhu, Lyu, Qing, Chen, Qinlang, Banjade, Rabin, Rudolph, Rachel Etta, Gabriel, Raefer, Habacker, Rahel, Risco, Ramon, Millière, Raphaël, Garg, Rhythm, Barnes, Richard, Saurous, Rif A., Arakawa, Riku, Raymaekers, Robbe, Frank, Robert, Sikand, Rohan, Novak, Roman, Sitelew, Roman, LeBras, Ronan, Liu, Rosanne, Jacobs, Rowan, Zhang, Rui, Salakhutdinov, Ruslan, Chi, Ryan, Lee, Ryan, Stovall, Ryan, Teehan, Ryan, Yang, Rylan, Singh, Sahib, Mohammad, Saif M., Anand, Sajant, Dillavou, Sam, Shleifer, Sam, Wiseman, Sam, Gruetter, Samuel, Bowman, Samuel R., Schoenholz, Samuel S., Han, Sanghyun, Kwatra, Sanjeev, Rous, Sarah A., Ghazarian, Sarik, Ghosh, Sayan, Casey, Sean, Bischoff, Sebastian, Gehrmann, Sebastian, Schuster, Sebastian, Sadeghi, Sepideh, Hamdan, Shadi, Zhou, Sharon, Srivastava, Shashank, Shi, Sherry, Singh, Shikhar, Asaadi, Shima, Gu, Shixiang Shane, Pachchigar, Shubh, Toshniwal, Shubham, Upadhyay, Shyam, Shyamolima, Debnath, Shakeri, Siamak, Thormeyer, Simon, Melzi, Simone, Reddy, Siva, Makini, Sneha Priscilla, Lee, Soo-Hwan, Torene, Spencer, Hatwar, Sriharsha, Dehaene, Stanislas, Divic, Stefan, Ermon, Stefano, Biderman, Stella, Lin, Stephanie, Prasad, Stephen, Piantadosi, Steven T., Shieber, Stuart M., Misherghi, Summer, Kiritchenko, Svetlana, Mishra, Swaroop, Linzen, Tal, Schuster, Tal, Li, Tao, Yu, Tao, Ali, Tariq, Hashimoto, Tatsu, Wu, Te-Lin, Desbordes, Théo, Rothschild, Theodore, Phan, Thomas, Wang, Tianle, Nkinyili, Tiberius, Schick, Timo, Kornev, Timofei, Tunduny, Titus, Gerstenberg, Tobias, Chang, Trenton, Neeraj, Trishala, Khot, Tushar, Shultz, Tyler, Shaham, Uri, Misra, Vedant, Demberg, Vera, Nyamai, Victoria, Raunak, Vikas, Ramasesh, Vinay, Prabhu, Vinay Uday, Padmakumar, Vishakh, Srikumar, Vivek, Fedus, William, Saunders, William, Zhang, William, Vossen, Wout, Ren, Xiang, Tong, Xiaoyu, Zhao, Xinran, Wu, Xinyi, Shen, Xudong, Yaghoobzadeh, Yadollah, Lakretz, Yair, Song, Yangqiu, Bahri, Yasaman, Choi, Yejin, Yang, Yichi, Hao, Yiding, Chen, Yifu, Belinkov, Yonatan, Hou, Yu, Hou, Yufang, Bai, Yuntao, Seid, Zachary, Zhao, Zhuoye, Wang, Zijian, Wang, Zijie J., Wang, Zirui, and Wu, Ziyi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting., Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench

Published
2022

2. QuALITY: Question Answering with Long Input Texts, Yes!

Author

Pang, Richard Yuanzhe, Parrish, Alicia, Joshi, Nitish, Nangia, Nikita, Phang, Jason, Chen, Angelica, Padmakumar, Vishakh, Ma, Johnny, Thompson, Jana, He, He, and Bowman, Samuel R.

Subjects
Computer Science - Computation and Language
Abstract

To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, our questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts. In addition, only half of the questions are answerable by annotators working under tight time constraints, indicating that skimming and simple search are not enough to consistently perform well. Our baseline models perform poorly on this task (55.4%) and significantly lag behind human performance (93.5%)., Comment: NAACL 2022

Published
2021

3. BBQ: A Hand-Built Bias Benchmark for Question Answering

Author

Parrish, Alicia, Chen, Angelica, Nangia, Nikita, Padmakumar, Vishakh, Phang, Jason, Thompson, Jana, Htut, Phu Mon, and Bowman, Samuel R.

Subjects
Computer Science - Computation and Language
Abstract

It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA). We introduce the Bias Benchmark for QA (BBQ), a dataset of question sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. Our task evaluates model responses at two levels: (i) given an under-informative context, we test how strongly responses reflect social biases, and (ii) given an adequately informative context, we test whether the model's biases override a correct answer choice. We find that models often rely on stereotypes when the context is under-informative, meaning the model's outputs consistently reproduce harmful biases in this setting. Though models are more accurate when the context provides an informative answer, they still rely on stereotypes and average up to 3.4 percentage points higher accuracy when the correct answer aligns with a social bias than when it conflicts, with this difference widening to over 5 points on examples targeting gender for most models tested., Comment: Accepted to ACL 2022 Findings. 20 pages, 10 figures

Published
2021

4. Mental Models and Interpretability in AI Fairness Tools and Code Environments

Author

Thompson, Jana, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Stephanidis, Constantine, editor, Kurosu, Masaaki, editor, Chen, Jessie Y. C., editor, Fragomeni, Gino, editor, Streitz, Norbert, editor, Konomi, Shin'ichi, editor, Degen, Helmut, editor, and Ntoa, Stavroula, editor

Published
2021
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7. School, Community, and Church Activities: Relationship to Academic Achievement of Low-Income African American Early Adolescents in the Rural Deep South

Author

Irvin, Michael J., Farmer, Thomas W., Leung, Man-Chi, Thompson, Jana H., and Hutchins, Bryan C.

Abstract

The relationship of participation in school, community, and church activities to the academic achievement (i.e., end-of-year school grades) of African American youth in rural low-income communities was examined. Participants included 280 students (177 girls and 103 boys) in the 7th and 8th grades of two public middle schools. Participation in school, community, and church activities was measured by participants' involvement in extracurricular activities in these contexts. Results indicated that after accounting for school activities and other control variables, church activities were related to academic achievement. Findings suggested that it is important to consider participation in the activities of contexts outside of school that may support the achievement of African American youth in the rural Deep South. (Contains 6 tables.)

Published
2010

8. Interpersonal Competence Configurations in Rural Appalachian Fifth Graders: Academic Achievement and Associated Adjustment Factors

Author

Farmer, Thomas W., Irvin, Matthew J., Sgammato, Adrienne N., Dadisman, Kimberly, and Thompson, Jana H.

Abstract

This study examined interpersonal competence configurations in a sample of 315 fifth-grade students (170 girls and 145 boys) from 8 elementary schools in the Appalachian region. Teachers completed the Interpersonal Competence Scale-Teacher (ICS-T) for each student. The ICS-T consists of 18 7-point Likert scales to assess academic achievement, aggression, internalizing behavior, popularity, social skills, and athletic ability/attractiveness. Teachers also completed scales pertaining to social adaptation. Students completed peer behavioral assessments, sociometric surveys, and social network surveys. Nearly 40% of students were in high-competence configurations characterized by high teacher ratings on academic, behavioral, and social adjustment. These configurations were associated with high end-of-year grades and standardized achievement test scores and with positive peer relationships. In contrast, 16% of girls and 27% of boys were in high-risk configurations characterized by high teacher ratings of academic, behavioral, and social difficulties. These configurations were associated with low end-of-year grades and standardized test scores and with social and behavioral difficulties that are predictive of later adjustment problems. We also identified moderate-risk configurations that tended to fall between high-competence and high-risk configurations in terms of their relation to academic achievement and peer relations. Implications for interventions are discussed. (Contains 13 tables.)

Published
2009
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9. Patterns of Middle School Adjustment and Ninth Grade Adaptation of Rural African American Youth: Grades and Substance Use

Author

Estell, David B., Farmer, Thomas W., Irvin, Matthew J., Thompson, Jana H., Hutchins, Bryan C., and McDonough, Erin M.

Abstract

The transition to high school has been identified as a potentially difficult time in adolescents' lives. Reductions in both academic and social functioning often accompany this transition. While these effects have been documented in urban youth, the move to high school has not been extensively studied in rural minority youth. Toward that end, the academic grades and substance use in ninth grade of 447 (184 male and 263 female) African-American adolescents from two rural counties in a state in the deep South were examined in relation to configurations of adaptation from sixth through eighth grade. Results indicate that individual with consistently positive patterns across middle school had higher grades and lower rates of substance use compared to individuals with persistent difficulties or those that transitioned to problem behavior. Many individuals who improved in their patterns of adaptation had relatively high grades, but also rather high rates of substance use in the ninth grade.

Published
2007
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10. Educating Out and Giving Back: Adults' Conceptions of Successful Outcomes of African American High School Students from Impoverished Rural Communities

Author

Farmer, Thomas W., Dadisman, Kimberly, Latendresse, Shawn J., Thompson, Jana, Irvin, Matthew J., and Zhang, Lei

Abstract

This study examined community adults' conceptions of successful early adult outcomes for rural African American adolescents from 2 low-resource communities in the Deep South. Focus groups were conducted with parents, teachers, and community leaders. Parents also completed semistructured phone interviews. The focus groups identified 2 general types of successful outcomes. One type involved youth leaving their hometowns to attain their educations and establish careers and then reconnecting with the community (i.e., "giving back"). The 2nd type involved youth establishing themselves in the community as employed adults to support themselves and their families. Parents also described a variety of successful outcomes related to education, employment, living arrangements, and family and community involvement. Barriers to success included involvement with drugs and alcohol, peer pressure, and a lack of community-level supports (e.g., jobs, youth programs, extracurricular activities, educational opportunities). (Contains 1 table.)

Published
2006

11. School Adjustment and the Academic Success of Rural African American Early Adolescents in the Deep South

Author

Farmer, Thomas W., Irvin, Matthew J., Thompson, Jana H., Hutchins, Bryan C., and Leung, Man-Chi

Abstract

This study examined the relationship between end-of-year grades and the academic, behavioral, and social characteristics of rural African American youth. Participants included 392 7th and 8th grade students from 2 rural middle schools in the south. Participants were African American and were from 2 communities that have child poverty rates exceeding 50% for public school students. Girls were more likely to have positive characteristics than boys. Academic, behavioral, and social difficulties were linked to low end-of-year grades, and positive characteristics were linked to high grades. Implications for supporting low-achieving African American students from low-resource communities are discussed. (Contains 4 tables.)

Published
2006

12. Who Do Students with Mild Disabilities Nominate as Cool in Inclusive General Education Classrooms?

Author

Rodkin, Philip C., Farmer, Thomas W., Acker, Richard V., Pearl, Ruth, Thompson, Jana H., and Fedora, Pledger

Abstract

Examined the nominations that elementary students with mild disabilities made for peers they perceived as cool. The total sample was comprised of 948 students (496 girls, 452 boys) from the metropolitan Chicago area and North Carolina and included 107 (11.3%) students with mild disabilities. Overall, students with mild disabilities nominated prosocial and athletic peers who affiliate with students with mild disabilities. However, aggressive boys who are central in the social network were highly likely to view aggressive peers as cool. Results are discussed in relation to implications for social interventions for students with mild disabilities.

Published
2006
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13. Strength-Based Assessment of Rural African American Early Adolescents: Characteristics of Students in High and Low Groups on the Behavioral and Emotional Rating Scale

Author

Farmer, Thomas W., Clemmer, Jason T., Leung, Man-Chi, Goforth, Jennifer B., Thompson, Jana H., Keagy, Kristin, and Boucher, Signe

Abstract

Early adolescents' strengths were examined in relation to factors that are associated with developmental risk or resilience in two rural low-income southern communities. The sample was comprised of 279 students (101 boys, 178 girls), all of whom were African American and reflected the public school attendance of this community. Parent reports on the Behavioral and Emotional Rating Scale (BERS) were used to assess strengths. BERS scores were examined in relation to academic, behavioral, and social characteristics of participants. For girls, total strength scores on the BERS were positively associated with indices of high competence and negatively associated with problematic characteristics. For boys, high and low total strength scores differentiated between youth who did and did not have risks associated with externalizing behavior problems. The results are discussed in terms of their implications for assessment and prevention.

Published
2005
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15. QuALITY: Question Answering with Long Input Texts, Yes!

Author

Pang, Richard Yuanzhe, primary, Parrish, Alicia, additional, Joshi, Nitish, additional, Nangia, Nikita, additional, Phang, Jason, additional, Chen, Angelica, additional, Padmakumar, Vishakh, additional, Ma, Johnny, additional, Thompson, Jana, additional, He, He, additional, and Bowman, Samuel, additional

Published
2022
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17. Smoke lifters

Author

Thompson, Jana L.

Abstract

[ILLUSTRATION OMITTED] Thanks for explaining the purpose of smoke lifters on steam locomotives in the February issue's Information Desk column. According to Jim Shaughnessy's book "Delaware & Hudson," the elephant […]

Published
2008

19. African American community of southeast Rocky Mount, North Carolina : an action-oriented community diagnosis final report

Author

Barber, Sharrelle, George, Allison, Petersen, Dustin, Tzeng, Janice, and Thompson, Jana

Abstract

During the fall of 2007 and spring of 2008, a team of five graduate students from the University of North Carolina at Chapel Hill���s School of Public Health conducted an Action-Oriented Community Diagnosis (AOCD) of the community in Southeast Rocky Mount. AOCD is a component of the curriculum for graduate students in the Department of Health Behavior and Health Education. Historically, the teaching team has been approached by community members seeking an assessment of their community. These community members then act as preceptors to student teams, introducing them to the community and aiding in our AOCD process. The process itself involves interacting with community members and service providers to systematically collect information about community strengths and challenges. The student team in Rocky Mount interviewed 19 service providers and 14 community members, conducted 2 focus groups and attended 12 community events. On April 12, the team hosted a community forum in Southeast Rocky Mount (SERM), at which findings were presented back to the community. Through break-out groups focused on discussing particular emerging themes, community members and service providers created action steps to address these themes. Hence, the AOCD process transformed from a diagnosis made by outsiders to a series of commitments by community participants to addressing their collective needs. At the beginning of the AOCD process, the student team conducted a secondary data review to learn more about Southeast Rocky Mount���s health, economics, crime and education status. The team then compared this data to that of the city of Rocky Mount, Edgecombe County and/or North Carolina depending upon the sources of data available. Rocky Mount is divided between two counties, Nash and Edgecombe, which at times affected the availability of data. The team also collected primary data through interviews and focus groups, which were guided by a predetermined set of questions. These discussions with community members and service providers provided much richer qualitative information. Team members transcribed and coded this data to determine which themes occurred most frequently. Identified strengths of the community included the people within the community and the history of a strong, thriving African American community in the area, among others. Meanwhile, challenges identified included: disparities between resources available in Nash County and Edgecombe County; lack of reliable public transportation; limited educational opportunities for youth and young adults; limited recreational and enrichment activities for youth; limited employment opportunities; dilapidated and sub-standard housing conditions; crime due to drugs and gang violence; and limited collaboration between community resources. Through consideration of these strengths and challenges, along with the coded interview data, the student team developed a list of emerging themes. These themes were presented to the eight community members and service providers that made up the forum planning committee. These members helped select the final themes to be used at the April forum. The initial list included: Youth, Education, Employment, Transportation, Crime, Housing/Cost of living, Parenting, and Communication between existing community resources. These eight preliminary themes were then narrowed down to the final five themes that we presented at the community form. The final themes selected for presentation to the community were: Youth, Employment, Connecting Community Resources, Crime, Housing. Approximately 65 individuals attended the community forum. Overall, participants were enthusiastic and optimistic when discussing the selected themes. At the end of the day, action steps were created for each theme and individuals personally committed to completing those steps. A future date was set for the community members and service providers to meet, without the student team, so that the AOCD process could continue within and among the community.

Published
2008
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20. A wave of success: Riders of all ages enjoy Tom Morey's creation

Author

Thompson, Jana

Subjects
Surfing -- Equipment and supplies, Surfboards -- Innovations
Abstract

SANTA ANA, Calif. _ Its creator refers to the bodyboard simply as the 'boogie.' Its detractors, when they're being nice, refer to its users as boogie flappers, spongers and speed […]

Published
1994

21. Competing for all-woman America's Cup team can be demanding, heartbreaking

Author

Thompson, Jana

Subjects
America's Cup races -- Competitions, Women sailors -- Competitions
Abstract

SAN DIEGO _ The training makes men cry. 'We had guys break into tears,' said unconventional billionaire Bill Koch, whose America3 team won the America's Cup in 1992 and will […]

Published
1994

22. Poll of principals finds most see benefit of school sports programs

Author

Thompson, Jana

Subjects
High school students
Abstract

SANTA ANA, Calif. _ The hierarchy of high schools supports the concept of sports contributing to education. The Colorado High School Activities compiled a report called 'The Case for High […]

Published
1994

24. Wrestling helps student get a grip on life, schoolwork

Author

Thompson, Jana

Subjects
High school students
Abstract

SANTA ANA, Calif. _ By the time he got to high school, Adolfo Chavez was tired, worn out, pooped. His hands hurt. Beating people up will do that to a […]

Published
1994

25. Reviewers

Author

Carlson, Corine K., Feagai, Hobie Etta, Fooshee, Erica, Golden, Susan, Gordy, Cynthia, Kuawogai, Vivian, Manworren, Renee C.B., Perry, Claudia, Petersen-Smith, Ann, Pitts, Jill, Ramirez, Janice, Rodgers, Cheryl C., Shanks, Chris, Thompson, Jana Wiscaver, Bible, Colleen W., Hamlisch, Betty, and Riedé, Nan

Published
2013
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26. Railway Post Office.

Author

Thompson, Jana L., Curlutu, Travis, Mahawash, Joe, Sornsin, Bill, and Piccirillo, Tom

Subjects
LETTERS to the editor, ELECTRIC equipment, RAILROAD cars, DECODERS (Electronics), INSTALLATION of equipment, RAILROADS
Abstract

Several letters to the editor are presented in response to articles in previous issues including "Delaware & Hudson," by Jim Shaughnessy in the February 2008 issue, "Installing sound decoders in steam engines," by Bill Darnaby in the June 2001 issue, and "Sound decoder installation," by Jack Burgess in the June 1999 issue.

Published
2008

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