Fancy Schools for Fancy People: Risks and Rewards in Fieldwork Research Among the Low German Mennonites of Canada and Mexico
Bibliographic record
Abstract
In the 1920s, conflict over schooling prompted the exodus of nearly 8000 Mennonites from the Canadian prairie provinces of Manitoba and Saskatchewan to Mexico and Paraguay; this is the largest voluntary exodus of a single people group in Canadian history. Mennonites—whose roots are found in the 1520s Reformation—are an Anabaptist, pacifist, isolationist ethnic, and religious minority group, and victims of a fledgling Canada’s nation-building efforts. It is estimated that approximately 80,000 descendants of the original emigrants have subsequently returned to Canada, where tensions over schooling have persisted. The tensions—then, as now—are rooted in a fundamentally different understanding of the purposes of education—and it is this tension that interests me as an ethnographer and education researcher. My research is concerned with assessing attitudes towards education within the Low German Mennonite (LGM) community in both Canada and Mexico. Too often academic research is presented as a tidy finished product, with little insight shed into the messy, highly iterative process of data collection. The purpose of this article is to pull back the curtain and discuss the messiness of the process, including security risks involved with methodology, site selection, research participants, and gaining access to the community.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".