Bibliographic record
Abstract
This article relates to the 2018 CSDH/SCHN conference proceedings. This paper outlines Michael Iantorno’s and Melissa Mony’s experiences with quantitative game analysis by summarizing the first year of development of the prototype ludomusicological database GameSound. To further the discussion, this article also summarizes and analyzes the work of fellow digital humanities scholar Jason Bradshaw, who applied intriguing types of tool-based analysis to BioShock Infinite. To conclude, the paper hypothesizes where this type of research could lead in the future: both for GameSound and for other projects using similar methods and methodologies. Résumé Cet article présente les expériences de Michael Iantorno et de Melissa Mony faites avec des analyses de jeu quantitatives, en résumant la première année de développement de la base de données prototype ludomusicale GameSound. Pour approfondir la discussion, cet article résume et analyse également l’oeuvre de Jason Bradshaw et de Dr. Adrienne Shaw, qui emploient des types intrigants d’analyses de jeu quantitatives et qualitatives dans leurs propres projets, respectivement la BioShock Infinite and Feminist Theory : A Technical Approach et The LGBTQ Video Game Archive. Pour conclure, cet article formule une hypothèse concernant l’avenir de ce genre de recherche : non seulement pour GameSound mais aussi pour d’autres projets qui se servent de méthodes et de méthodologies similaires. Mots-clés: études de jeux; la ludomusicologie; humanités numériques; recherche quantitative; bases de données
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".