Still Crazy about Teaching after All These Years: An Interview with Dick Gorman
Bibliographic record
Abstract
Richard Gorman received his BA in philosophy from the University of Notre Dame and his PhD in educational psychology from Fordham University. Over the years, he has taught at several colleges and universities—both in the east and west—and is currently a member of the Psychology Department at Central New Mexico Community College. He has taught courses in developmental, educational, and cognitive psychology, but is currently concentrating on the introductory psychology course. His publications include a text in the psychology of classroom learning, an introduction to Piaget's theory for teachers, and, more recently, an introductory psychology text. He has given presentations and workshops on discovery learning to various audiences including the National Institute on the Teaching of Psychology (NITOP). Brian L. Burke is an Assistant Professor of Psychology at Fort Lewis College, a public liberal arts college in Durango, Colorado. He is a licensed clinical psychologist whose principal academic interests include motivational interviewing and college teaching. He regularly attends teaching conferences, twice winning the Doug Bernstein Award for innovative teaching ideas (NITOP 2004, 2005). He also won the New Faculty Teaching Award at Fort Lewis College in 2005. He teaches a wide variety of courses, including introductory psychology, personality, abnormal, counseling, research methods, history of psychology (via a European trip), and forensic psychology. Brian hails originally from Montreal, Canada, and received his PhD from the University of Arizona in 2003.
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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 | 0.021 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".