Bibliographic record
Abstract
• Research objectivesThis article focuses on two goals: based on the study of free-to-play video games, it aims to determine mobile users’ perceptions of models mixing free and paid content, and to identify the methods of resistance employed by the players.• MethodThe authors based their work on various concepts like free use, psychological reactance, resistance and cheating. A qualitative methodology was defined, blending netnography with semi-structured interviews (n=18).• ResultsThe research sheds light on the observed methods of resistance and on the ambiguous relationships between mobile users and these models. It also enabled the characterization of a number of resistance profiles.• DiscussionThe study formulates recommendations for mobile video game publishers, as well as for marketing managers in charge of mobile apps. It encourages them to emphasize the transparency of their business models, to be less persuasive/directive by promoting an alternative to paid purchases and, lastly, to offer an added-value experience through experiential benefits.• OriginalityThis work reveals and explains an aggressive, controversial phenomenon of resistance: strategies for circumventing the rules by means of deviant behaviour (cheating and neutralization strategies). It will fuel discussions of users’ relationships to free-to-play games and shines a light on value-shaping mechanisms within the context of mobile apps.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.909 | 0.868 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".