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Record W4408672269 · doi:10.1080/21681376.2025.2476633

Hidden treasures: estimating intergenerational wealth transfers in rural Canada

2025· article· en· W4408672269 on OpenAlex
Alex Petric, Ryan Gibson

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRegional Studies Regional Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of GuelphUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of CanadaOntario Agri-Food Innovation Alliance
KeywordsEconomic geographyGeographyEconomicsDemographic economics

Abstract

fetched live from OpenAlex

Intergenerational wealth transfers hold potential benefits for community finance in regions facing financial/development challenges due to demographic and economic shifts. We construct a model for estimating wealth transfers based on available Canadian data, with emphasis on data for primary dwelling values. We apply our model across census divisions over 10-, 20-, and 50-year periods and analyse resulting patterns. Results show regional variation in estimated transfer sizes, with urban proximity as a key factor. Findings suggest roles for rural community initiatives (like community foundations) to locally embed wealth over time, particularly given the increasing global mobility of wealth and finance.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.031
GPT teacher head0.278
Teacher spread0.247 · 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