It Often Takes Two Income Earners to Raise a Farm: On-farm and Off-farm Employment in Kansas
Bibliographic record
Abstract
Despite media depictions of U.S. family farms with the entire family engaged in household chores and farming, the reality is that income is often generated from multiple sources. For many farm families, working both on-farm and off-farm is important. Focusing on Kansas, where the majority of farms are family owned, survey and interview data are used to examine if households with off-farm employment differ from those without it. The results suggest that if a farm operation has sales of less than $100,000 annually and it is smaller than 100 acres, or the farmer is younger, more educated or started farming more recently, the chances that they have a household member working off-farm are greater. In addition, numerous challenges to being successful in farming were identified by farmers, and we discuss the implications for farm families. Keywords: farming; off-farm employment; farm households; United States; Kansas; off-farm income --------------------------------------------------------- Resume Malgre les representations des familles agricoles americaines ou toute la famille a sa responsabilite dans les tâches domestiques, la realite est que le revenu provient generalement de plusieurs sources. Pour beaucoup de familles agricoles, travailler sur la ferme et en dehors de la ferme est important. En se concentrant sur le Kansas, ou il y a une majorite de familles agricoles proprietaires, les donnees d'enquetes et d'entrevues sont utilisees pour examiner si les foyers avec un emploi hors-ferme different de ceux qui n'en n'ont pas. Les resultats suggerent que si une exploitation agricole a des ventes inferieures a $100,000 par an et est plus petite que 100 acres, ou que le fermier est jeune, plus eduque ou vient recemment de rentrer dans l'activite agricole, les chances pour qu'il ait un membre de la famille travaillant a l'exterieur de la ferme sont plus elevees. De plus, de nombreux defis pour reussir dans l'agriculture furent identifies par les fermiers et nous en discutons les implications pour les familles agricoles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".