Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wilfred Amaldoss (“ Pricing Prototypical Products ”) is a professor of marketing at the Fuqua School of Business of Duke University. He holds an MBA from the Indian Institute of Management (Ahmedabad), and an M.A. (applied economics) and a Ph.D. from the Wharton School of the University of Pennsylvania. His research interests include behavioral game theory, experimental economics, advertising, pricing, new product development, and social effects in consumption. His recent publications have appeared in Marketing Science, Management Science,the Journal of Marketing Research,the Journal of Economic Behavior and Organization, and the Journal of Mathematical Psychology. His work has received the John D. C. Little and the Frank Bass awards. Tammo H. A. Bijmolt (“ Optimizing Retail Assortments ”) is a professor of marketing research at the Department of Marketing and director of the research school SOM, Faculty of Economics and Business Administration, University of Groningen, the Netherlands. His research interests include conceptual and methodological issues such as retailing, loyalty programs, pricing, and meta-analysis. His work has appeared in prestigious journals, such as the Journal of Marketing Research, Journal of Marketing, Journal of Consumer Research, International Journal of Research in Marketing (IJRM), Psychometrika, and the Journal of the Royal Statistical Society Series A. He serves as an associate editor for IJRM. Michael Braun (“ Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories ”) is an associate professor of marketing at the Cox School of Business at Southern Methodist University. He earned his Ph.D. from the Wharton School of the University of Pennsylvania, and he holds an A.B. with honors in economics from Princeton University and an MBA from the Fuqua School of Business at Duke University. He is a noted expert on the statistical analysis of large and complex customer databases; he has written on, spoken on, and taught about management topics such as sales forecasting, customer retention and valuation, marketing return on investment, social networking models, segmentation and targeting strategies, online advertising, and insurance decisions. From 2006 to 2013, he served on the marketing faculty of the MIT Sloan School of Management. Doug J. Chung (“ The Dynamic Advertising Effect of Collegiate Athletics ”) is an assistant professor of business administration in the Marketing Unit at Harvard Business School. He received his Ph.D. in management from Yale University and is also the recipient of the ISMS Doctoral Dissertation Award, ISBM Doctoral Support Award, and the Mary Kay Doctoral Dissertation Award. His research primarily focuses on sales force management and incentive compensation. Prior to pursuing a career in academics, he served as an officer and platoon commander in the South Korean Special Forces before continuing on to a variety of industry positions with several multinational companies. Imran S. Currim (“ Information Processing Pattern and Propensity to Buy: An Investigation of Online Point-of-Purchase Behavior ”) is the Chancellor's Professor and Associate Dean at the Paul Merage School of Business at University of California, Irvine. He received his B.Eng. from Victoria Jubilee Technical Institute of the University of Bombay, an MBA from the University of Wisconsin, and M.S. (statistics) and Ph.D. (business) degrees from Stanford. He received two American Marketing Association awards: the William O'Dell Award for a paper published in the Journal of Marketing Research and the Houghton Mifflin Distinguished Teaching in Marketing Award. He has published 40 articles on consumer choice models in the leading field journals, served as an associate editor for Marketing Science and Management Science, and served on the editorial boards of the Journal of Marketing Research and the International Journal of Research in Marketing. For the past five years he has served as associate dean of master's, executive, and undergraduate programs; the Wall Street Journal cited him as a Favorite Professor in an Executive MBA Program, and Business Week ranked his executive MBA marketing class third in the world. Chuan He (“ Pricing Prototypical Products ”) is an associate professor of marketing at the Leeds School of Business, University of Colorado at Boulder; he is also visiting associate professor of marketing at the Cheung Kong Graduate School of Business in Beijing, China. He holds a Ph.D. in marketing from Washington University in St. Louis and an M.A. in economics from the University of Toronto. His fields of specialization and research include advertising, search, pricing strategies, and channel contracts. His recent studies have appeared in Marketing Science, the Journal of Marketing Research, and the Economic Journal. He serves on the editorial board of Marketing Science. Zhongsheng Hua (“ Commentary—On ‘Equilibrium Returns Policies in the Presence of Supplier Competition’ ”) is a professor at and vice dean of the School of Management at University of Science and Technology of China. He has published extensively on supply chain management, channel management, and production research. Ivan Jeliazkov (“ Information Processing Pattern and Propensity to Buy: An Investigation of Online Point-of-Purchase Behavior ”) is an associate professor of economics and statistics at the University of California, Irvine. His research is in the area of Bayesian econometrics, with an emphasis on modeling, Markov chain Monte Carlo estimation, model comparison, and discrete data analysis. Yongquan Lan (“ Commentary—On ‘Equilibrium Returns Policies in the Presence of Supplier Competition’ ”) is a Ph.D. candidate affiliated with the joint Ph.D. program between the City University of Hong Kong and the University of Science and Technology of China. His research interest is on operations management and operations/marketing interface research. Yanzhi Li (“ Commentary—On ‘Equilibrium Returns Policies in the Presence of Supplier Competition’ ”) is an associate professor at the City University of Hong Kong. He received his Ph.D. and B.S. from the Hong Kong University of Science and Technology and Tsinghua University, respectively. His research interest is primarily on operations and supply chain management, and he has been recently focusing on interface research between operations and marketing and operations and finance. Ofer Mintz (“ Information Processing Pattern and Propensity to Buy: An Investigation of Online Point-of-Purchase Behavior ”) is an assistant professor of marketing at the E. J. Ourso College of Business at Louisiana State University (LSU). He completed his Ph.D. in marketing at the University of California, Irvine; M.Sc. in finance at the University of London; and BBA in marketing at Texas A&M University. Before coming to LSU, he was a visiting faculty member at the Interdisciplinary Center (IDC), Herzliya, Israel, for the spring 2012 semester. His research on marketing strategy/analytics and social media/online marketing has appeared (or is forthcoming) in Marketing Science and the Journal of Marketing. In addition, his teaching in social media/online marketing and international marketing has received high peer evaluation grades, and his courses have also been highlighted by the media. Wendy W. Moe (“ Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories ”) is an associate professor of marketing and director of the M.S. in Marketing Analytics program at the Robert H. Smith School of Business, University of Maryland. She holds a Ph.D., M.A., and B.S. from the Wharton School at the University of Pennsylvania, as well as an MBA from Georgetown University. She is a recognized expert in online marketing and social media and has been on the faculty at the University of Maryland since 2004. Prior to that, she was on the faculty at the University of Texas at Austin. In addition to her academic work, she has consulted for numerous corporations and government agencies, helping them develop and implement state-of-the-art statistical models in the context of Web analytics, social media intelligence, and forecasting. Amit Pazgal (“ Co-Creation with Production Externalities ”) is a professor of marketing at the Jones Graduate School of Business, Rice University. He received his Ph.D. from the Kellogg School of Management, Northwestern University. His current research focuses on the analysis and characterization of optimal price-setting procedures employed by firms in various strategic environments. His research has appeared in the leading marketing, management, operations, and economics journals. Raghunath Singh Rao (“ Conspicuous Consumption and Dynamic Pricing ”) is an assistant professor of marketing at the McCombs School of Business at the University of Texas at Austin. He received a master's in applied economics and a Ph.D. in business administration from the University of Minnesota. His research interests include information asymmetry and bounded rationality issues in marketing in relation to substantive topics such as durable goods markets, pricing, sales management, and innovation. His research has been published in the Journal of Marketing Research, Marketing Science, and the Journal of Marketing. He was honored as a Marketing Science Institute (MSI) Young Scholar in 2011. Robert Ridlon (“ Favoring the Winner or Loser in Repeated Contests ”) is an assistant professor of business economics and public policy in the Kelley School of Business at Indiana University. He received his doctoral degree in business economics from Indiana University, where
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it