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Record W4389195793 · doi:10.2991/978-94-6463-298-9_4

Financial Crises and Inequality: Exploring the Relationship between Delinquency and Greater Polarization

2023· book-chapter· en· W4389195793 on OpenAlex
Jiaxuan Lan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in economics, business and management research/Advances in Economics, Business and Management Research · 2023
Typebook-chapter
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsThe King's UniversityWestern University
Fundersnot available
KeywordsJuvenile delinquencyInequalityPolarization (electrochemistry)EconomicsPolitical sciencePsychologyCriminologyMathematicsChemistryMathematical analysis

Abstract

fetched live from OpenAlex

High inflation, rising concerns around cost of living, the topic of finance and its related crises has seemingly been on the rise in the last few decades.Through understanding how financial/banking crises can be linked to inequality, it can be perceived as to whether inequality is simply inevitable and whether there are steps that can be taken in order to reduce its relevant extent.This essay will focus on leading up to, and following the Great Recession of 2007/2008 and find whether rising economic inequality has resulted in greater polarization overall.There is evidence to suggest that financial crises can cause and can result in an aftermath of great inequality however these effects may have varying levels of impact as well.Not only is understanding the relationship important, but the past can also provide answers for the future, especially relating to how inequality has been reduced and what methods have been drawn up at present to mitigate some of these issues.

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.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.005
Open science0.0010.005
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 head0.178
GPT teacher head0.341
Teacher spread0.163 · 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