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Record W4404462158 · doi:10.1177/09637214241283163

The Psychology of Poverty: Current and Future Directions

2024· article· en· W4404462158 on OpenAlexaff
Y Park, Yuen Wan Ho, Kristina Hallez, Supreet Kaur, Mahesh Srinivasan, Jiaying Zhao

Bibliographic record

VenueCurrent Directions in Psychological Science · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of British Columbia
FundersCentro de Excelencia en Geotermia de Los Andes
KeywordsPsychologyPovertyCurrent (fluid)Cognitive psychologySocial psychologyEconomic growth

Abstract

fetched live from OpenAlex

An emerging literature on “the psychology of poverty” suggests that the experience of poverty itself has psychological consequences, some of which may make escaping poverty more difficult. We synthesize the evidence base from both psychology and economics using an organizing framework comprising four sets of mechanisms: cognitive function, mental health, beliefs, and preferences. We discuss the strength of the evidence supporting both how poverty affects these four mechanisms and how these four mechanisms in turn affect poverty. As our review shows, the existing evidence has clearly established proof of concept that psychological factors exist in the experience of and response to poverty. However, there is still a lack of evidence on whether these effects are meaningful in magnitude and lead to the perpetuation of poverty. We conclude by summarizing promising future directions for research that could help close these evidence gaps, with important implications for the design of poverty reduction policies.

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)0.020
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0040.006
Science and technology studies0.0030.011
Scholarly communication0.0080.020
Open science0.0030.005
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0150.002

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 head0.048
GPT teacher head0.444
Teacher spread0.397 · 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 designTheoretical or conceptual
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

Citations5
Published2024
Admission routes1
Has abstractyes

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