Schooling across Contexts: The Educational Realities of Old Colony Mennonite Students
Bibliographic record
Abstract
This paper narrates the schooling experiences of the Old Colony Mennonites (OCM) across two contexts based on my first-hand observations as a principal in a rural, southwestern Ontario school and on a research trip to the Cuauhtémoc, Chihuahua, area of Mexico. The OCM children described in this paper attend public school in southwestern Ontario and travel regularly to Mexico where many of the families hold property or visit family. Thus, during one calendar year, these OCM children often attend schools in two countries with important differences in the use of language(s) and literacy practices, expectations in the classroom, and even the meaning of playing outside. This reality, which requires OCM students to adapt to the expectations of two very different learning cultures, is an important facet in the life of these children, whose experiences of school and education are vastly different. Using ethnographic methods and case study tools including photos, I describe the educational settings of Ontario and Chihuahua where OCM students are schooled. Further, I illustrate how the diaspora from Russia to Manitoba, Canada, the subsequent migration to Mexico, and then the return of the OCM to Canada (this time Ontario) was partially predicated on the need to find places where their beliefs about education could be enacted, a search that continues to the present.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".