Multidimensional measures of farmer well-being: A scoping review
Bibliographic record
Abstract
Abstract Determinants of farmer well-being can be derived from objective and subjective measures of social components, environmental sustainability indicators, and quality of life indices, as well as the multiple scales that farms and farmers operate. Yet, despite multiple frameworks on farmer well-being, the extent to which farmer-expressed values are used in the development of farmer well-being indicators is unclear. Challenges can arise from extracting indicators that are insufficiently grounded in place, or that inadequately incorporate context and biocultural relations and practices. Here in this scoping review, we synthesize the methodologies in the literature on assessing farmer well-being and identify the extent to which farmer well-being domains are derived from values expressed directly by farmers. We consolidated and coded 92 papers to respond to the following questions: (1) What are the most frequent farmer well-being domains in published studies? (2) What methods are used to elicit multidimensional farmer well-being domains? (3) Do well-being domains used in the literature adequately reflect a biocultural context, including place-based influences on well-being? Our results show that economics and social relationships are frequent domains of how farmer well-being is identified and assessed. These domains tend to be measured simultaneously, while less common domains, such as governance and place, are rather isolated. A suite of methods was used to assess well-being domains, ranging from basic surveys to in-depth participant observation. Yet, we identify gaps in the methods for deriving farmer well-being indicators. Specifically, methods that refer to farmer-expressed values were rare and domains identified through a place-based approach were often not recorded, but, arguably, critical in developing multidimensionality of farmer well-being. We show that while the translocal approach is well represented in established well-being frameworks, farmer expression is not foundational in well-being assessments but is needed in order to center farmer values when generating indicators of well-being.
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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 | 0.038 | 0.169 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".