Bibliographic record
Abstract
For close to two hundred years, families and individuals across Ontario have travelled down country roads and gathered to enjoy seasonal agricultural fairs. Though some features of township and county fairs have endured for generations, these community events have also undergone significant transformations since 1850, especially in terms of women’s participation. Cultivating Community tells the story of how women’s involvement became critical to agricultural fairs’ growth and prosperity. By examining women’s diverse roles as agricultural society members, fair exhibitors, performers, volunteers, and fairgoers, Jodey Nurse shows that women used fairs’ manifold nature to present different versions of rural womanhood. Although traditional domestic skills and handicrafts, such as baking, needlework, and flower arrangement, remained the domain of women throughout this period, women steadily enlarged their sphere of influence on the fairgrounds. By the mid-twentieth century they had staked out a place in venues previously closed to them, including the livestock show ring, the athletic field, and the boardroom. Through a wealth of fascinating stories and colourful detail, Cultivating Communities adds a new dimension to the social and cultural history of rural women, placing their activities at the centre of the agricultural fair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".