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Record W3120793539 · doi:10.1371/journal.pone.0244569

Happy without money: Minimally monetized societies can exhibit high subjective well-being

2021· article· en· W3120793539 on OpenAlexaff
Sara Miñarro, Victoria Reyes‐García, Shankar Aswani, Samiya Ahmed Selim, Christopher Barrington‐Leigh, Eric D. Galbraith

Bibliographic record

VenuePLoS ONE · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsMcGill University
FundersH2020 European Research CouncilEuropean Commission
KeywordsMonetizationHappinessSubjective well-beingLife satisfactionPerceptionEconomicsDemographic economicsPsychologySocial psychologyMacroeconomics

Abstract

fetched live from OpenAlex

Economic growth is often assumed to improve happiness for people in low income countries, although the association between monetary income and subjective well-being has been a subject of debate. We test this assumption by comparing three different measures of subjective well-being in very low-income communities with different levels of monetization. Contrary to expectations, all three measures of subjective well-being were very high in the least-monetized sites and comparable to those found among citizens of wealthy nations. The reported drivers of happiness shifted with increasing monetization: from enjoying experiential activities in contact with nature at the less monetized sites, to social and economic factors at the more monetized sites. Our results suggest that high levels of subjective well-being can be achieved with minimal monetization, challenging the perception that economic growth will raise life satisfaction among low income populations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.033
GPT teacher head0.269
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2021
Admission routes1
Has abstractyes

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