Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".