Playing to Grow. Roundtable Interview on Games, Education, and Character
Bibliographic record
Abstract
In this roundtable interview moderated by Paul Darvasi, lecturer at the University of Toronto and co-founder of Gold Bug Interactive, Owen Gottlieb and Matthew Farber discuss research and practice at the intersection of religion, character education, and games in schools. Gottlieb is an associate professor at the Rochester Institute of Technology, founder and lead faculty at the Initiative in Religion, Culture, and Policy at the MAGIC center, and founder and director of the Interaction, Media, and Learning Lab at RIT, where he specializes in interactive media, learning, religion, and culture. Farber is an associate professor of educational technology and coordinator of K12 and Secondary Teacher Education Programs (STEP) at the University of Northern Colorado, where he also co-directs the Gaming SEL Lab. He writes for Edutopia, has authored several books and papers, and has collaborated with UNESCO MGIEP, the iThrive Games Foundation, and Games for Change. This conversation occurred over Zoom on 10 October 2022, and is sometimes specific to how schools, education, and educational television function historically and currently in the United States.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".