Online games as a medium of cultural communication: An ethnographic study of socio-technical transformation
Bibliographic record
Abstract
Online games as a medium of cultural communication: An ethnographic study of socio-technical transformation."This dissertation explores the place and meaning of online games in everyday life.In South Korea, online games are a prominent part of popular culture and this medium has come under public criticism for various societal ills, such as Internet addiction and a hopeless dependence upon online games.Humanistic accounts of Information-Communication Technology (ICT) usage are still a minority body of research.All too often, studies of engagement with technology reduce questions to their basic variables and social aspects are omitted in the name of science.Exactly how has it come to pass that online games have come to occupy such a prominent place in the media ecology in South Korea and yet not been replicated in other national contexts?The first chapter discusses addiction as it pertains to online games and suggest some scholarly support for the viewpoint that the rhetoric surrounding a biomedical interpretation of online game addiction may not be the most appropriate way to address problems that have been typically laid at the feet of online gaming (or any other new form of media).The second chapter transitions into discussing my rationale for approaching South Korea as a fieldsite, the ethnographic methodology employed, and how this
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".