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Record W3033086507 · doi:10.1177/0003122420920647

Elaborating on the Abstract: Group Meaning-Making in a Colombian Microsavings Program

2020· article· en· W3033086507 on OpenAlexafffund
Laura Doering, Kristen McNeill

Bibliographic record

VenueAmerican Sociological Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsGovernment (linguistics)PreferenceOddsFinancial servicesDisseminationPublic relationsProcess (computing)Meaning (existential)MarketingBusinessSociologyEconomicsFinancePolitical sciencePsychology

Abstract

fetched live from OpenAlex

Access to formal financial products like savings accounts constitutes a hallmark feature of economic development, but individuals do not uniformly embrace these products. In explaining such financial preferences, scholars have focused on institutional, cultural, and material factors, but they have paid less attention to organizations and small groups. In this article, we argue that these factors are crucial to understanding financial preferences. We investigate a government-sponsored microsavings program in Colombia and find that participants became less interested in banking services over the course of the program, even as they gained access to appropriate accounts and their savings increased. Turning to qualitative data to understand this curious finding, we show that organizational efforts to disseminate abstract information about banking triggered a process of “elaboration” among group members, leading many to develop financial preferences at odds with those promoted by the government. This study integrates insights from economic sociology, organizational theory, and microsociology to advance theories of financial preference. In doing so, we reveal how organizational efforts to compress information, followed by group efforts to personalize and expand upon the information, can shape preferences and potentially undermine organizational goals.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.298
Teacher spread0.254 · 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 designNot applicable
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

Citations8
Published2020
Admission routes2
Has abstractyes

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