“GG” Good Gaming Wordlists: Insight Into Single-Player Commercial Off-the-Shelf Games and Action-Roleplay Game Vocabulary
Bibliographic record
Abstract
The purpose of this study is to create two pedagogical wordlists for videogames to be used by ESL/EFL teachers and learners.Using a multimethod design, the present research utilizes both quantitative and qualitative measures.A keyness analysis using the Minimal Ratio index provided by the Keyworder software was conducted to investigate a game corpus' distinctive words.The corpus of ten videogame scripts from Rodgers and Heidt (in press) (5.7 million tokens) was compared to the SUBTLEXus TV/movie corpus (50.5 million tokens) to identify flemmas that are significantly more frequent in videogames.With Schmitt's (2019) call for a better understanding of the vocabulary found in games and game-genres in mind, two wordlists were created: a common game wordlist and an action-roleplay game (ARPG) wordlist to investigate variation in game vocabulary.To facilitate vocabulary acquisition, the listed words were coded for ludic/diegetic properties and patterns.Results regarding learning are discussed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".