Dynamics of farm entry and exit in Canada
Bibliographic record
Abstract
Abstract The dynamics of entry and exit are examined across different categories of farms depending on the timing of entry and/or exit through a detailed panel data set on Canadian agriculture. The decomposition highlights the differences in the groups of farms and provides information affecting entry and exit beyond what can be inferred from net exit numbers. While aggregate values show a gradual fall in farm numbers over time and suggest a sector in decline, the decomposition reveals that approximately one-third of farms in each census are new entrants but only half of these will be in operation by the time of the next census. The results of the analysis suggest that many of the factors that increase the probability of entry also increase the probability of exit; smaller operations, producing vegetable/horticulture goods, located in more densely populated regions, are more likely to enter the sector but also to leave farming. Multigeneration involvement and a possible succession plan also contribute to the longevity of the farm operation after it has been launched. The results also highlight the decline of the mid-size operations and the growing importance of large farms in the overall share of production.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".