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Record W2040476496 · doi:10.1163/15691497-12341263

Finding the Edges of Payday Lending

2013· article· en· W2040476496 on OpenAlexaff
Rob Aitken

Bibliographic record

VenuePerspectives on Global Development and Technology · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDebtPoliticsEconomic JusticeGlobalizationPolitical economyBusinessMarket economyEconomicsPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Abstract Although virtually unknown in 1990, payday lending has become an important and profitable credit practice over the past two decades. Because of this growth, and because it has been implicated in ever-increasing levels of debt among the poor, payday lending has been confronted by an active political movement which has had some success in imposing forms of regulation in the United States and abroad. This paper argues, however, that this activism has failed to address the increasingly globalized nature of payday lending. Over the past five years, in particular, payday lending has become a thoroughly globalized practice deeply connected to and enabled by liberalized global financial markets. This paper reflects on the distance between the activism that now confronts it in national or subnational contexts and this increasingly global reach of payday lending. It concludes by making the case for a more fully globalized conception of global financial justice.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.013
Scholarly communication0.0090.009
Open science0.0010.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.001

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 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

Citations3
Published2013
Admission routes1
Has abstractyes

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