Bibliographic record
Abstract
This book would have been impossible without the help of numerous people.I am indebted to the farmers who opened their doors and shared their stories.Not only did I learn from them about life in the Dutch countryside, but they dispelled any preconceptions I had about farming in Canada as a way of life and a means of making a living.I also want to thank my research assistants: Michael Fallon interviewed farmers of Dutch background in the Niagara peninsula and in Brant County, while working on his doctorate in history.Lisa Dent-Couturier, then an MA student in rural extension, conducted interviews in Essex County, and Margaret VanderSchot, a farmer, helped me in the southern half of Perth and the northern tip of Oxford counties.Their initials will appear in footnotes, when I refer to those interviews.Michael Johnston's contribution included mapping out the locations of Dutch farmers in Grey County.His MA thesis on the perception of recent Dutch immigrant farmers is cited in this book.Fiep de Biep did one interview and conducted observations in Waterloo and Wellington counties as part of her training in fieldwork methods while enrolled in a BA program at the University of Guelph.At various times
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.138 | 0.005 |
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; both teacher heads agree on what is shown here.
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".