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Record W3124500453

Intergeneration Transfers and Retiring Farmers

2007· preprint· en· W3124500453 on OpenAlexaboutno aff
John Caldwell, David M. Culver

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureBusinessAgricultural economicsCensusAgricultural scienceEconomies of scaleFirst generationEconomicsGeographyPopulationMarketing
DOInot available

Abstract

fetched live from OpenAlex

The percentage of farmers who are approaching retirement age is increasing. The census of agriculture shows that in 2001 there were a larger percentage of farmers over 55 years of age than was the case in the previous censuses. The transferring of the assets held by these farmers to the next generation has important policy implications for the structure of Canadian agriculture. It also raises several policy questions for future research. Using data from 2005 Farm Financial Survey this paper examines the transfer of assets for both one and multi-generation farms. We have identified 73,900 farms where the oldest operator is 55 or older. Of these farmers 18,800 are operated by more than one generation of farmers. In the case of these farms the next generation is already involved in the farm business. The remaining 55,100 farms are operated by only one generation of farmers. The total assets based on market value for the one generation farms are estimated to be $47 Billion. The assets which are expected to be transferred to the next generation total $33.4 Billion. The majority of these farms are expected to be bought up by the multi-generation farms to achieve economies of scale or to be purchased by new entrants as lifestyle farms. In the case of multi-generation farms the total assets owned are estimated to be $40 Billion. The assets which are expected to be transferred to the next generation total $34.1 Billion. These farms are expected to stay within the family and be purchased by the next generation. They will continue to be operated by the next generation and in some instances on a larger scale.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.296
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 designOther design
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
Published2007
Admission routes1
Has abstractyes

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