"And in Mexico we found what we had lost in Canada" : Mennonite immigrant perceptions of Mexican neighbours in a Canadian newspaper, 1922-1967
Bibliographic record
Abstract
ÄbstractThe first conservative, Low German-speaking Mennonites from Canada arrived in Mexico in 1922, setting up colonies that were to be apart from the rest ofsociety in order that they could enjoy complete religious fieedom.Over the ensuing years, however, this isolationist position has been tempered by a number of factors that have served to place this ethno-religious group into regular contact with the surrounding Mexican world.The case of the Mennonites living in Mexico serves to illuminate relations between immigrants and a host society.The connections that have been formed across the cultural, economic, and religious boundaries dividing Mennonites from their Mexican counterparts provides an opportunity not only to observe the nature ofinter-ethnic relations but also to examine the process by which such associations help to foster an evolving ethnicity in an immigrant group.As Kathleen Conzen and David Gerber argue, eth.nicity is not a stagnant concept but is in continuous flux.A minority's identity and self-perception, therefore, is defined in relation to the majority and is constantly renegotiated through these interactions.By examining the letters written by Mennonites in Mexico and published in the Manitoba-based newspaper, Dle Steinbach Posf, it becomes evident that Mennonite encounters with Mexicans did not weaken the immigrant group's ethnic and religious identity.Instead, these interactions in the marketplace, in times of conflict, and in interpersonal relationshìps served to create a communal Mennonite identity and strengthen their self-perception as an ethnic minority living on foreign soil.Acknorvledgments First and foremost, I would like to thank my advisor, Royden Loewen, for his advice, guidance, and diligence throughout every stage of this thesis.Without my introduction to the world of Mennonite history as an undergraduate student in his class six years ago, I never would have come to realise the richness of the fìeld; without his encouragement, fiiendship, and patience this project would not have been possible.I would also like to express my appreciation to the members of my advisory committee, Hans Wemer, David Burley, and Kathleen Venema, for their careful evaluation and insightful comments, and for making my thesis defence such a positive and enriching experience.My
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.030 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".