Bibliographic record
Abstract
the deep gratitude i want to express to all the colleagues, friends and family members who contributed to this manuscript transcends the customary ritualistic gesture of ordinary acknowledgments.Almost every page of what follows has been touched by their ideas, suggestions and scrutiny, reflecting the role each has played in my life's journey.Not only did discussions with friends and colleagues here in Boston, and with others across the North American continent, encourage me to tell my story but these conversations, replete with recollections and insights, have also enriched my narrative.In the first two chapters I've relied on written and oral family histories.I am grateful to Gerald Brunk, whose knowledge of Mennonite history guided the accuracy of my account in the first chapter.In reconstructing early family life and circumstances in Duchess, Tapping the Bow by R. Groos and L.N. Kramer proved most helpful in providing a valuable account of the impact of irrigation in the region; contributions from Dick Martin and his wife June were also especially helpful, as were conversations with J. Robert Ramer and with Sam Martin and
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.479 | 0.257 |
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; the direct Gemma label and the distilled Codex classifier 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".