Happy just because. A cross-cultural study on subjective wellbeing in three Indigenous societies
Bibliographic record
Abstract
While cross-cultural research on subjective well-being and its multiple drivers is growing, the study of happiness among Indigenous peoples continues to be under-represented in the literature. In this work, we measure life satisfaction through open-ended questionnaires to explore levels and drivers of subjective well-being among 474 adults in three Indigenous societies across the tropics: the Tsimane' in Bolivian lowland Amazonia, the Baka in southeastern Cameroon, and the Punan in Indonesian Borneo. We found that life satisfaction levels in the three studied societies are slightly above neutral, suggesting that most people in the sample consider themselves as moderately happy. We also found that respondents provided explanations mostly when their satisfaction with life was negative, as if moderate happiness was the normal state and explanations were only needed when reporting a different life satisfaction level due to some exceptionally good or bad occurrence. Finally, we also found that issues related to health and-to a lesser extent-social life were the more prominent explanations for life satisfaction. Our research not only highlights the importance to understand, appreciate and respect Indigenous peoples' own perspectives and insights on subjective well-being, but also suggests that the greatest gains in subjective well-being might be achieved by alleviating the factors that tend to make people unhappy.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".