How can Squadland motivate people to adopt sustainable behaviours through its metaverse?
Bibliographic record
Abstract
Squadeasy, founded in France in 2014 with a move-to-earn app, launched a metaverse called Squadland in 2022, with a goal of increasing the company’s positive impact on the planet. When app users engaged in fitness activity in the real world, they earned tokens to buy land and other digital assets (NFTs) in Squadland, thereby improving the environment both inside the app and in the real world; Squadeasy bought land in the real world to mirror users’ actions in the metaverse. In this way, users could contribute to positive social change and have a sustainable effect on the world. This case discusses users’ motivations to engage in this metaverse, through the lens of Self Determination Theory. First, rather than fun and rewards, identified regulation is the relevant motivation to trigger commitment to the metaverse as it related to personal values and self-identity. Second, three external situational factors (autonomy, competence, and relatedness) positively increase commitment and will help users stay with the app. The metaverse fits well with these three external situational factors and can help achieve actual sustainable change.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".