Old people, video games and french press: A topic model approach on a study about discipline, entertainment and self-improvement.
Bibliographic record
Abstract
Over the past few years, the French mainstream press has paid more and more attention to "silver gamers", adults over sixty who play video games. This article investigates the discursive and normative paradigms that underlie the unexpected enthusiasm of the French mainstream press for older adults who play video games. We use mixed methods on a corpus of French, Swiss and Belgian articles that mention both older people and video games. First, we produce topics, that is, sets of words related by their meanings and identified with a Bayesian statistical algorithm. Second, we cross the topic model results with a discursive analysis of selected articles. We preface the topic modeling's conclusions with a discussion of the representations of older people and video games in European French-language mainstream media. Our analysis explores how the press coverage of older people who play video games simultaneously erases moral panic about video games and reinforces the discourse of "successful ageing".
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".