Bibliographic record
Abstract
Michael K. Barbour is an assistant professor of instructional technology at Wayne State University in Detroit, Michigan. He received his bachelor’s and master’s degrees from Memorial University of Newfoundland and his PhD from the University of Georgia. His research interests focus on the effective delivery and support of rural K-12 students learning in online learning environments.Teklu Abate Bekele is a doctoral candidate in the Institute for Educational Research at the University of Oslo.Robert Hannafin is an associate professor in the Department of Educational Psychology at the University of Connecticut. He received his PhD in learning and instructional technology at Arizona State University in 1994. His research interests include examining open learning environments and technology integration in public school classrooms.Andri Ioannou is a doctoral student in the educational technology program at the University of Connecticut. She received a MA in educational technology from the University of Connecticut and a BS in computer science from the University of Cyprus. Ioannou is interested in how technology can improve learning, productivity, networking and collaboration. Her research interests include Web-based learning and online learning environments.Douglas A. Kranch is a professor of computer information systems at North Central State College, Mansfield, OhioMichael Paul Menchaca is an assistant professor in the Department of Educational Technology at the University of Hawaii at Manoa.Anthony A. Piña is dean of online studies for the Sullivan University System, Kentucky’s largest private higher education institution. He has been in the field of instructional technology since 1987 and has worked in both academic and industry settings. He has taught at the secondary, community college, and university levels and has developed and taught many online and hybrid (blended) courses. Piña and has been a consultant to Fortune 500 corporations, small businesses, government agencies, university consortia, K-12 schools, and the U.S. Military. He is a past president of the Division of Distance Learning of the Association for Educational Communications & Technology and of the Community College Association for Instruction & Technology. Pina is a frequent presenter at professional conferences and has published one book and several journal articles on instructional technology and distance learning.
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 | 0.005 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.517 | 0.308 |
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".