Postdigital Videogames Literacies: Thinking With, Through, and Beyond James Gee’s Learning Principles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article is a collective response to the 2003 iteration of James Paul Gee’s What Video Games Have to Teach Us About Learning and Literacy . Gee’s book, a foundational text for those working in game studies, literacy studies, and education, identified 36 principles of ‘good learning’ which he argued were built into the design of good games, and which have since been used to unsettle the landscape of formal education. This article brings together 21 short theoretical and empirical contributions which centre postdigital perspectives to re-engage with, and extend, the arguments first raised by Gee regarding the relationship between videogames and learning. Organised into five groups, these contributions suggest that concepts and attitudes associated with the postdigital offer new thinking tools for challenging grand narrative claims about the educative potential of technologies while also providing rich analytical frames for revisiting Gee’s claims in terms of postdigital videogame literacies.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it