Virtual Curriculum: Digital Games as Technologies of AestheticExperience and Portential Spaces
Bibliographic record
Abstract
In this paper, I am interested in the following question: What can we learn in the strange spaces of digital games?This is not the same as asking what digital games can teach us or what we learn from digital games.Rather, I am interested in thinking about what it is we learn as we play digital games.This question of learning has less to do with the content of the game-although that is an important question too-and more to do with what the experience of the game allows us to think or not think and to be or not be.What do the place and time of the digital game offer us?Educational research on digital games (of which there is still very little) and, more generally, on media education and educational technology is often preoccupied with questions of 'media effects'.What do media do to us?How do they shape our children's behaviour?Or, as I phrased it above, what do media teach us?This approach is at the root of concerns about whether or not video game content encourages violent behaviour and fears that the frequent use of digital media will somehow negate the benefits of literate media, such as reading, writing, and comprehension.Studies of media effects are primarily concerned with outcomes and media effects research lends itself nicely to an emphasis on outcome-oriented curriculum by asking, how can this new media help us achieve or avoid a certain set of outcomes?Digital simulations and games along with other uses of digital media, such as net-art and hypertext fiction, are not easily understood by researchers whose purpose is to examine media effects, and neither are they easily ad-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".