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Record W4289755119 · doi:10.1002/jcpy.1323

Rethinking scarcity and poverty: Building bridges for shared insight and impact

2022· article· en· W4289755119 on OpenAlexaff
Chris Blocker, Jonathan Z. Zhang, Ronald Paul Hill, Caroline Roux, Canan Corus, Martina Hutton, Joshua D. Dorsey, Elizabeth A. Minton

Bibliographic record

VenueJournal of Consumer Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsConcordia University
Fundersnot available
KeywordsScarcityPovertyLeverage (statistics)Resource scarcityPopulationResource (disambiguation)Development economicsSociologyConstruct (python library)EconomicsEconomic growthNatural resource economicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Resource scarcity is a powerful construct in social sciences. However, explanations about how resources influence overall wellbeing are difficult to generalize since much of the research on scarcity focuses on relatively affluent marketplace conditions, limiting its usefulness to large segments of the global population living in poverty. Conversely, poverty research provides cultural insights into resource deprivation, yet it stops short of explaining the systematic variation of scarce resources among impoverished individuals. To bridge these intellectual silos and advance a deeper understanding of scarcity, we integrate resource scarcity research, which builds upon a psychological tradition to understand various forms of everyday deprivation, with poverty research, which builds upon a sociological tradition to understand extreme and enduring deprivation. We propose a novel framework that integrates the concept of consumption adequacy and clarifies resource scarcity's forms, intensity, duration, and dynamic trajectories. We leverage this framework to generate a research agenda, and we propose ways to stimulate dialog among scarcity and poverty scholars, policymakers, and organizations to help inform impoverished life circumstances and generate effective solutions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.054
GPT teacher head0.382
Teacher spread0.328 · 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 teacher head, not a consensus.

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

Citations55
Published2022
Admission routes1
Has abstractyes

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