Creating with (Un)Limited Possibilities: Normative Interfaces and Discourses in Super Mario Maker
Bibliographic record
Abstract
This paper explores how the creative expression of players is framed within Super Mario Maker (Nintendo, 2015). Dispelling the promises of “endless possibilities” (Nintendo, 2015) with which the game is marketed, this article argues instead that a player’s creativity is oriented, limited, and influenced by the interface of the game (its possibilities, and impossibilities), the paratext supplied by Nintendo (advertising, user guide, and tutorials), the reception, as well as the appraisal of levels by the community of players within the closed social platform of the game. In order to analyze this process of “normativization,” the following article begins by proposing an actualization of theories of participatory culture as defined by Matt Hills (2002), Henry Jenkins (2006), Sam Ford, and Joshua Green (2013). From these remarks, this paper also proposes to locate some of Super Mario Maker’s normative elements that have an influence on players’ creations, using as a starting point McIntyre’s work on creativity (2012), Albera’s concept of “amateur-dispositive” (2011), Kline et al.’s “Three Circuits of Interactivity” (2003), and Consalvo’s gaming capital (2007). Finally, this paper analyzes certain recurring motifs found in Super Mario Maker’s user-generated levels that serve to benefit what I call the “paradigm of difficulty,” a pattern well-known within the video game medium since its infancy.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.051 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".