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Record W2901678059 · doi:10.1111/bjso.12303

Managing a moral identity in debt advice conversations

2018· article· en· W2901678059 on OpenAlexfundno aff
Nicole Andelic, Clifford Stevenson, Aidan Feeney

Bibliographic record

VenueBritish Journal of Social Psychology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersEconomic and Social Research CouncilQueen's UniversityQueen's University Belfast
KeywordsDebtConversationNegotiationSocial psychologyPsychologyPublic relationsBusinessPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Previous research has found that stigma can be a barrier to service use but there has been little work examining actual service encounters involving members of stigmatized groups. One such group are those with problematic or unmanageable debts. Providing advice to members of this group is likely to be particularly difficult due to the stigma associated with being in debt. Using conversation analysis and discursive psychology, this study examines 12 telephone advice conversations between debt advisors and individuals in debt. Both clients and advisors oriented to the negative moral implications of indebtedness and typically worked collaboratively to manage these issues. Clients often claimed a moral disposition as a way to disclaim any unwanted associations with debt, but could find it difficult to reconcile this with an insolvency agreement. Moreover, the institutional requirements of the interaction could disrupt the collaborative management of stigma and advisors could manage the subsequent resistance from clients in either client-centred or institution-centred ways. The findings suggest that the products offered by debt advice agencies, as well as the manner in which they are offered to clients, can either help or hinder debtors negotiate the stigma-related barriers to service engagement.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.867

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 studies0.0010.000
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 head0.065
GPT teacher head0.467
Teacher spread0.402 · 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.

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

Citations18
Published2018
Admission routes1
Has abstractyes

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