Bibliographic record
Abstract
Preparing this book has been a journey of excitement, anticipation, and fulfillment.It would never have been written without the involvement and support of a number of people.I am grateful to the Low German Mennonites whom I met throughout the years, including the many who agreed to be interviewed and to talk about their lives, knowledge, and beliefs.I thank the research advisory committee members, whose dedication to understanding LG Mennonites and to sharing that knowledge with me helped ensure the successful completion of the projects.Mennonite Central Committee Canada was very helpful, particularly John Janzen, former Low German coordinator, as were Dave and Margaret Penner, who hosted me during my visit to Durango Colony, Mexico.The research assistants in the various projects not only collected data but shared their perspectives about a number of topics related to what is discussed here.The final two projects were coordinated by HaiYan Fan, whose attention to detail, insights, and pure joy at being in the field made for interesting conversations but also some fun in exploring and learning together about Mennonite communities.My thanks also go to the undergraduate research assistants who worked on the projects and to the numerous family members, friends, and colleagues who cheered me on from the sidelines.I would like to thank the clinical agencies with which I worked: Alberta Health Services, Southern Health-Santé Sud 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 | 0.002 | 0.009 |
| 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 | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.252 | 0.179 |
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".