In and out of control: Learning games differently
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".