Research Exchange - November 17, 2020 "New Directions at MISQ" with Andrew Burton-Jones, moderated by Cynthia Beath
Bibliographic record
Abstract
After recently being named the new Editor in Chief at MIS Quarterly, Dr. Andrew Burton-Jones sit down with Cynthia Beath to discuss new directions at the renowned journal. Burton-Jones will be discussing the journals current vision and impact on IS scholarship and knowledge.\nAndrew Burton-Jones is a Professor of Business Information Systems at the UQ Business School, University of Queensland. He obtained his BCom (Hons) and M. Information Systems from the University of Queensland and his Ph.D. from Georgia State University. Prior to returning to UQ, he was an Associate Professor at the University of British Columbia. Andrew conducts research on how organizations can use information systems more effectively, how to improve systems analysis and design methods, and how to improve theories and methods in the IS discipline. Recently, much of his work has focused on healthcare contexts. Andrew has taught a variety of courses in the USA, Canada, China, and Australia. He is a Fellow of the Association for Information Systems and incoming Editor-in-Chief of MIS Quarterly.\nModerator Cynthia M. Beath is a Professor Emerita of Information Systems at the McCombs School of Business at UT Austin and an AIS Fellow. She received her MBA and PhD degrees from UCLA. She recently published Designed for Digital, a book about how organizations redesign themselves for the digital era, with colleagues at the Center for Information Systems Research at MIT. Her research has been published in MIS Quarterly and Information Systems Research, and she has served as senior editor for both journals. An active advocate for her professional community, she initiated the field’s first junior faculty consortium, served as chair of a division of the Academy of Management, held a number of positions on the Council of the AIS, and helped found MISQ Executive.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.621 | 0.568 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".