In and out of control: Learning games differently
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
In this paper we make use of the theoretical resources of actor network theory as a ‘frame’ within which to organize video data we have been collecting on playing, and more specifically, on girls learning to play, digital games. Through a microanalysis of interaction, we closely examine intersecting trajectories of control -- self, other, and technology -- within the context of game play. Using MAP, a software program that supports multimodal analysis, we offer an illustrated account of the microgenesis of competence in collaborative, technologically-supported gameplay, drawing attention to developmentally significant behavioural regularities which, because they are embodied and not necessarily cognitive-linguistic in character, have not typically been evidenced in research on collaborative learning. A particular contribution of this paper is its study of group play, a relatively under-studied topic in gameplay research, and a perspective that has allowed us to look specifically at the phenomenon of the distributed development of competence central to learning in and through collaborative play.
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) | 0.000 | 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