Developing a Community: Qualitative Approaches to Understanding the Role of Community Engagement in Gameswork
Bibliographic record
Abstract
Through multiple qualitative approaches, this dissertation contributes to understanding the increased role of addressing, engaging, and managing online communities in gameswork. It pays particular attention to how individual actors – such as game developers, content creators, community managers, and game journalists – collectively react to shifting industry trends that prioritize community engagement and building. It contributes to the literature on games by highlighting the experiences and perspectives of those working within the industry – such as community managers and game developers – as their industry undergoes significant shifts in priorities. In addition, it contributes to media and platform studies by examining the impacts on the production and consumption of media when audiences demand more intimate and direct access to creators. It pays specific attention to the workers who act as the filter between those who produce and those who consume. \n \nThis dissertation draws together four individual projects with distinct methodologies, research partners, and questions to illustrate the impacts of this shift. Chapter 2 examines critical games journalism to show how a lack of investment in community engagement leads to a breakdown of the community. Chapter 3 uses qualitative interviews and observation of drag content creators to show how they grapple with building their online communities amidst changing platform dynamics. Chapter 4 uses qualitative interviews with game developers to highlight how they choose to or choose not to work with content creators as they adapt to new priorities in their industry. Chapter 5 uses qualitative interviews with community managers to examine how their work has changed, continues to change, and leaves lingering anxieties and questions about the future of their work. \n \nThese individual projects are tied together through the complementary theme of servitization (Vandermerwe & Rada, 1988; Weststar & Dubois, 2022), which captures the trend of traditionally individually produced, packaged, and consumed products moving to a system of continuous access and consumption. As gameswork produces more products designed as a service for consumers, it changes the needs and expectations of gaming communities. I argue that this increased emphasis on community changes priorities for those working within creative and cultural industries that have implications for developers, community managers, and players. As these priorities change, new concerns arise regarding the working conditions, career, and educational pathways for those in community-focused roles.
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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 | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".