The Farm Wife Mystery School: Women's use of social media in the contemporary North American urban homestead movement
Bibliographic record
Abstract
Within the larger North American food security movement, self-professed ‘urban homesteaders’ have been tearing up their backyard lawns to plant vegetable gardens and install chicken coops in search of greater self-sufficiency and independence from industrial agriculture and the corporate food chain. Participants are most often white, middle-class professionals who have no previous experience in food production or preservation and they turn to the internet and social media for support and instruction. This article considers the online community created by the urban homesteading movement as a so-called community of practice, an educational space where adult learners never meet face-to-face, but where significant informal, self-directed learning is taking place. Women's urban homesteading blogs are examined in the context of social movement learning to determine whether the use of social media as an educational and community-building tool is aiding women in furthering their food security goals. Women's gender roles in this contemporary movement are also considered, and this analysis suggests that using social media may be helping to bridge the traditional isolation expressed by previous generations of women, to reinforce a sense of political purpose to home-based food production, and to potentially create an income source for the family.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".