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Record W4311660475 · doi:10.1177/00222437221146521

Helping Those That Hide: Anticipated Stigmatization Drives Concealment and a Destructive Cycle of Debt

2022· article· en· W4311660475 on OpenAlexafffund
Michael Moorhouse, Miranda Goode, June Cotte, Jennifer Widney

Bibliographic record

VenueJournal of Marketing Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsWestern UniversityWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDebtConsumer debtVariety (cybernetics)Social psychologyContext (archaeology)PsychologyCredit cardEconomicsFinance

Abstract

fetched live from OpenAlex

Debt accumulation has been linked to materialism, impulsivity, shortsightedness, self-control, and lifestyle preferences. However, applying stigma theory allows novel insights into debt accumulation for middle-class individuals who access a variety of credit-related products. The authors define anticipated stigmatization of debt as the negative judgment and discrimination an individual expects to experience because of their consumer indebtedness. Results from a series of studies demonstrate that although financial stress motivates behaviors designed to reduce debt, debtors who anticipate stigmatization perform a variety of concealment behaviors (secrecy, social spending, and help avoidance) that hinder debt reduction and have negative effects on well-being. To understand how to help these individuals, the authors collaborated with a financial education company, designing a field experiment to examine the efficacy of a behavior change course. Individuals who anticipated stigmatization and formed new social connections in a community-based condition reduced their consumer debt. Although the emotional effect of community-based support has been examined in other stigma contexts, this study is the first to investigate the effect on well-being in a debt context and link social benefits to actual behavior change in terms of debt reduction behaviors and debt repayment.

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)0.016
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.177
GPT teacher head0.499
Teacher spread0.322 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

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