Bibliographic record
Abstract
This paper seeks to explore the role of play in teacher education and ongoing professional development.Specifically, this paper examines the potential place of digital games in formal education, and how and why teachers need to play these games.Each new technology brings with it different forms of knowing and learning, as well as different things to know and learn.Digital games are a new technology.When considering linguistic literacy, we normally assume it to include an ability to speak, understand, read, and write.Literacy includes an ability to both consume and produce.Several reasons to advocate games literacy will be discussed in this paper, including advantages gained through new connections between learners and teachers that become possible as a result.Teachers must become literate in this new medium, and, if teachers are to become "games literate", that must include actual first-hand game experience.Teachers must play games.This paper will explore ways that this can be accomplished.You can discover more about a person in an hour of play than in a year of conversation. Plato Anyone who makes a distinction between games and learning doesn't know the first thing about either. Marshall McLuhanWe don't stop playing because we get old... we get old because we stop playing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".