How can Squadland motivate people to adopt sustainable behaviours through its metaverse?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it