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Record W4366978921 · doi:10.1016/j.heliyon.2023.e15759

How Canadian seniors make decision about insolvency?

2023· article· en· W4366978921 on OpenAlexaffabout
Samir Amine, Wilner Predelus

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsInsolvencyEngineeringBusinessFinance

Abstract

fetched live from OpenAlex

This paper analyzes the growing insolvency phenomenon among Canadian seniors. It aims at situating the rise of insolvencies among seniors in the context of the demographic transition to understand the cause of their indebtedness. Furthermore, it feeds the scientific voice in the current debate to explain the rise of insolvencies among seniors. Our study is based on data of 1,285,000 insolvent debtors collected by the Canadian Office of the Superintendent of Bankruptcy (OSB) from 2008 to 2018. We observed that the rise in the share of insolvencies filed by seniors is consistent with the progression of their share in the total population. Therefore, the relative increase observe in seniors' insolvencies is attributable to their growing share in the total population, and not necessarily to an increase in seniors' insolvencies. Given the aging of the Canadian population and its impact on the labour market, policy makers should adjust the insolvency system to be more responsive to seniors' needs and align with other public policies.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.026
GPT teacher head0.220
Teacher spread0.194 · 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 designQualitative
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

Citations0
Published2023
Admission routes2
Has abstractyes

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