Using Big Data Tools and Techniques to Study a Gamer Community: Technical, Epistemological, and Ethical Problems
Bibliographic record
Abstract
This paper discusses an exploratory approach taken by researchers in the fields of semiotics and communications in order to not only share a specific research experience, but also help build a research sector that combines game analytics with social sciences. The main objective of our research was to define parameters of digital identity within the framework of the study of an online video game player community. To this end, we examined several constitutive elements of digital identity, namely the effects of the “avatar” apparatus on the identity of users, online interactions, and the meaning of “living together” in the digital world. We used both qualitative and quantitative methodologies: a semiotic analysis of the game, a discursive analysis of the forum, semi-structured interviews, and an automated analysis of big data sets. In this paper we will focus on the automated analysis of big data sets, addressing two key points: the working method developed by the research team, and the achievement of the research objectives by merging quantitative and qualitative perspectives together. Following a summary of the research approach, this article will present the methodological, epistemological, and ethical difficulties that may be encountered in studying a player community with this type of research approach.
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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 | 0.170 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".