Bibliographic record
Abstract
Until recently exergaming was seldom a topic of research. The technology that makes exergaming possible was not available to consumers. In 2006, Nintendo released the Wii gaming system. This new system allowed for interactive physical movement beyond simple hand held play. The Wii system contained hardware and software that responded to movements of the player's body through the tracking of hand held controllers and movements of the lower extremities using floor based hardware. Exergaming appears poised to continue its foray into popular culture for better or worse. After nearly a decade of research a single theory for exergames has not been suggested. Previous researchers have relied on existing theories to guide them. Over a dozen theories have been used by authors of research into exergaming. With all of this research and the many theories that have been used it is time for an examination of these theories as to their relevance for exergaming. This paper endeavors to review the existing literature to identify what theories are being used in research and to delineate what the components of each theory are. A literature review was conducted using the Trident International University online library. This library allowed access to the ProQuest Summon® Service search engine which allowed for a search of multiple libraries including Blackwell, Gale, LexisNexis, Academic, Sage, Springer, Emerald, ProQuest, Taylor & Francis, IEEE, and Project Muse resulting in a search of more than 6,800 publishers and 94,000 journal and periodical titles. There are over a dozen theories found in the literature on exergaming. This paper endeavors to examine how often each theory appears in the literature while providing a brief overview of each theory. In the final analysis, the theory chosen for exergame research will be determined by the type of study undertaken.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".