Bibliographic record
Abstract
Based on the case of Game Studies, this thesis explores the socialization, interactions and relationships between researchers and video game professionals. This work was conducted in three different countries: France, Switzerland and the Canadian provinces of Quebec and Ontario. It is based on interviews, observations (scientific events, game jams and playtests) and a three-year ethnography conducted in a non-profit scientific organization. Our work raises the issue of how researchers and professionals socialize and build relationships. How do people from the scientific and industrial worlds tell their relationships? What is the role of social activities in meeting and creating long-term ties? How does the analysis of gender relations contribute to understanding the activities studied? Our study shows that the socialization, the personal and professional ties that are created between researchers and professionals participate in developing Game Studies and build bridges between research and the video game industry. The interviews and the observed social activities thus reveal the social dynamics between individuals of these two social worlds, showing the permanent porosity between them. Moreover, the gender-based dynamics highlighted in this study show the existence of specific social mechanism, linked to video games and to virilist values that go beyond the game environment. More broadly, this analysis contributes to a reflection to be pursued on the internal functioning of the scientific and industrial worlds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".