DeFragging Regulation: From putative effects to ‘researched’ accounts of player experience
Bibliographic record
Abstract
In line with the conference theme for 2013, this paper introduces a research project that is seeking to ‘defragment’ research dealing with player experiences. Located at an intersection between humanities, social sciences and computer sciences, our research aims to achieve greater receptiveness for accounts of games that emphasise “the relationship between the structure of a game and the way people engage with that system” (Waern, 2012, p.1) in the context of game regulation. Working specifically within the context of the New Zealand classification system, which possesses a legally enforceable age-restriction system, the project seeks to strengthen regulators capacity to utilize Section 3(4) of the current Classification Act and support the employment of concepts such as ‘dominant effect’, ‘merit’ and ‘purpose’ when classifying games (OFLC, 2012). Extending an established appreciation within game studies for the way games produce polysemic performances and readings, this paper draws on our mixed methods approach in an exploration of the nature of a players’ experience with Max Payne 3 (Rockstar Vancouver). In doing so, we illustrate the different dynamics at play in its expression and use of violence - dynamics that fail to achieve expression when games are considered more generally within political and social realms.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".