Bibliographic record
Abstract
Research Background Movement-based digital games typically make it clear whose movement representation belongs to which player. In contrast, we argue that selectively concealing whose movement controls which representation can facilitate engaging play experiences. We call this "innominate movement representation" and explore this opportunity through our game "i-dentity", where players have to guess who makes everyone's controller light up based on his/her movements. Our work reveals five dimensions for the design of innominate movement representation: concealing the association between movement and representation; number of represented movements; number of players with representations; location of representation in relation to the body and technical attributes of representation. We also present a set of strategies for how innominate representation can be embedded into a play experience. With our work w expand the range of digital games. Research Contribution This work contributes to understanding game and interaction design for social and physical play. Drawing inspiration from research on ubiquituous computing, embodied interaction and games, this research introduces the concept of innominate representations for digital bodily play Research Significance The significance of this research is attested to by the following indicators: its being presented (Long Paper), exhibited (Interactivity) and is a finalist for the Student Game Competition at the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2014 Proceedings and Extended Abstracts, Toronto, Canada) (peer-reviewed, 22.8% acceptance rate, previously ERA A ranked); and has been presented (Long Paper) at the ACM Conference on Interactive Entertainment (IE 2013 Proceedings, Melbourne, Australia). It has also been featured in the official ACM CHI 2014 promotional trailer at http://chi2014.acm.org/, which has been watched by over 5,000 people on youtube at http://www.youtube.com/watch?v=iN1wLizrsKY
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.
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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.189 | 0.050 |
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".