Bibliographic record
Abstract
During one several-month period in the writing of this book, I frequently drove past an attractive sign near downtown Waterloo, Ontario, that pronounced 'Mennonite Women' in large bold lettering.The sign advertised an exhibit of original paintings by artist Peter Etril Snyder.Well-known especially for his watercolour and oil depictions of Old Order Mennonites in southwestern Ontario, Snyder had drawn together a collection of original artwork on Mennonite women.When a co-worker and I were in the gallery one day to purchase a gift for a colleague, we couldn't help but chuckle at ourselves, two 'Mennonite women,' in an exhibit devoted to, well, us!The sign proclaimed a certain monolithic theme-as if one could capture Mennonite women as a meaningful organizing focus-though the paintings themselves hinted at the diversity of experience and perspective that one might begin to discover if the women portrayed could give voice to their stories.At times I have felt it presumptuous for me to write a book on Mennonite women, as if I could even hope to paint a picture in words in which every Mennonite woman could see herself or her female ancestors, in the same way that I struggled with some amusement to find myself in that exhibit of paintings.x
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.444 | 0.249 |
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".