Family Response to the Farm Crisis: A Study in Coping
Bibliographic record
Abstract
T HE RECENT farm crisis createdmajor upheavals for rural families and communities. Although the worst of the farm crisis seems to be over, the rural economy remains fragile, and long-term trends will continue to dis-place rural families (Erb & Petroski, 1989; Norman, 1989; Office of Tech-nology Assessment, 1986). As a result, understanding how these events af-fected families and how these families survived continues to be important for social workers who serve rural com-munities and families who have been displaced from these communities. Using a family crisis model, this article examines ways in which rural families coped with the disruptions created by the farm crisis. Family Crisis Model According to models of family stress and coping, the impact of a potentially stressful event is influenced by the ongoing interaction among three basic factors: (1) the nature of the event; (2) the resources available to members of the family; and (3)the ways in which both the event and potential resources are defined by family members, the community, and the cultural group. A crisis ensues when available resources cannot prevent events from creating major disruptions in the lives of the family (McCubbin & Patterson, 1982). Stressful Event The term "farm crisis " refers to a lengthy period of major economic de-pression in large ' agricultural areas of the United States and Canada from the late 1970s to the late 1980s. A series of international and domestic events and policy decisions contributed to long-term economic problems in the agri-cultural sector. These problems resulted in layoffs in agriculturally related in-dustries, an increase in poverty rates,
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".