Quiet in the Land: A Novel
Bibliographic record
Abstract
Mennonite fiction is experiencing a renaissance as a new generation of writers and scholars emerge onto the Canadian literary landscape. Authors like Miriam Toews, David Bergen, Rudy Wiebe, and Sandra Birdsell, have established a strong bedrock of Mennonite literature, from which a wellspring of fiction-writing about this specific ethno-religious identity is growing amongst a younger group of writers.\nA Millennial Mennonite writer myself, my novel-thesis, Quiet in the Land, is a contemporary Mennonite Kunstlerroman that explores the lives of three generations of Mennonite women artists living on the Canadian prairies. Through the lives of my fictional characters, I paint a picture of what critic and scholar Magdalene Redekop refers to as the “porous boundaries” of urban and rural Mennonite communities in Manitoba.\nThe question that guided the writing of Quiet in the Land is how Mennonite women across generations imagine and reimagine their identities as artists and/or mothers within Mennonite mythologies of place. The novel spans a reasonable length of time from the 1970’s to present day and features a range of women characters living and working in artistic, domestic, and agricultural contexts. Through a fictional lens, my literary project resists nostalgic tropes and instead focusses on how creative practices produce generative, enlivening spaces within the lives of Mennonite women who choose to make their home on the Western prairies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".