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
Background We contend that a conceptual conflation of simulation and imitation persists at the heart of claims for the power of game-based simulations for learning. Recent changes in controller-technologies and gaming systems, we argue, make this conflation of concepts more readily apparent, and its significant educational implications more evident. Aim This article examines the evolution in controller technologies of imitation that support players’ embodied competence, rather than players’ ability to simulate such competence. Digital gameplay undergoes an epistemological shift when player and game interactions are no longer restricted to simulations of actions on a screen, but instead support embodied imitation as a central element of gameplay. We interrogate the distinctive meanings and affordances of simulation and imitation and offer a critical conceptual strategy for refining, and indeed redefining, what counts as learning in and from digital games. Method We draw upon actor-network theory to identify what is educationally significant about the digitally mediated learning ecologies enabled by imitation-based gaming consoles and controllers. Actor-network theory helps us discern relations between human actors and technical artifacts, illuminating the complex inter-dependencies and inter-actions of the socio-technical support networks too long overlooked in androcentric theories of human action and cognitive psychology. Conclusion By articulating distinctions between simulation and imitation, we show how imitative practices afforded by mimetic game controllers and next-generation motion-capture technologies offer a different picture of learning through playing digital games, and suggest novel and productive avenues for research and educational practice.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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