«Let’s Go Deep into the Game to Save Our Planet!» How an Immersive and Educational Video Game Reduces Psychological Distance and Raises Awareness
Bibliographic record
Abstract
Climate change appears to be the ecological issue which benefits from the most attention in the literature, compared to equally alarming situations such as plastic pollution. In fact, waste management issues took a new step with the recent discovery of microplastics in human blood for the first time, as it used to be a hypothesis. Instead of separating those questions, some researchers tend to consider that a link exists between the effects of global warming and plastic degradation in the ocean. Research focusing on the construal-level theory and the psychological distance explain the lack of public interest in the environmental crisis. However, recent studies highlight the empirical support of the psychological distance instead of the CLT, especially regarding climate change, but a few studies explore the psychological distance related to plastic pollution. With that in mind, any means to reduce the perceived psychological distance regarding environmental issues such as plastic pollution might increase their sensitivity and motivation to act. Moreover, the change of habit could be induced by a new event that would disrupt someone’s daily life according to the habit discontinuity hypothesis, and the use of immersive media such as video games might be the solution. Given numerous possibilities of creation with the scenarios, gameplay, public of interest and gaming contexts, video games also influence motivation, engagement and learning ability. We can also find specific components and mechanisms from game design in media that do not focus on entertainment first but on pedagogical purpose: serious games. Thus, this study investigates how immersive media might reduce specific psychological distance dimensions and trigger emotions using an educational video game on plastic pollution, which might play a major role in changing ones’ daily habits. The research uses a qualitative method centered on semi-structured individual interviews and the experimentation of a video game named Plasticity. Results support all the propositions and show that different types of immersion might reduce each dimension of the psychological distance, which is a first, reinforcing environmental awareness and new intentions of pro-environmental behavior. Other areas of discussion are furthered explored.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".