Family and Gender in the Transformation of the Countryside
Bibliographic record
Abstract
This special issue presents articles which explore how gender relations evolved in rural families in the context of the ongoing transformation of rural life at large and farming in specific related to the global trends of modernisation. The effects of modernisation on rurality are contextual. The case studies in this volume represent diverse patterns of modernisation along different paths to industrialisation (cases studies from Northern, highly industrialised versus Southern late industrialised countries) as well as along different paths to capitalism (see the case studies from post-socialist societies). Large-scale socioeconomic forces led to the transition and dissolution of the “traditional farm family”. New forms of existence emerge for rural families complementing and even replacing the role of farming. The volume elucidates how gender relations are formed in rural families representing a diversity of emerging rural family life-styles, such as one-man farms, summer farms, farms engaged with tourism or having complementary off farm incomes. Gender relations are also studied in the light of changing gender ideologies, such as the case of post-socialist societies. The case studies in the volume provide empirical and theoretical frameworks exploring how the relation between the ongoing transformation of family farms (such as processes of masculinisation vs feminisation) and of rural families (such as retraditionalisation vs detraditionalisation) can be related to changing gender relations (women’s empowerment vs reconstitution of gender inequalities).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".