Lessons for Good Citizenship: Creating Attachments and Sense of Belonging in the Multi-ethnic Countryside of Kazakhstan
Bibliographic record
Abstract
This thesis focuses on citizenship education in the post-Soviet Republic of Kazakhstan. By drawing on insights in anthropology and education, I examine the state project to construct the ideal of good citizenship and its transformation through daily practices that shape the social life of school, community, family, and school ecology. My work is grounded in ethnographic fieldwork, most of which was conducted in the multi-ethnic countryside in northern Kazakhstan. Based on testimonies of my project’s participants, including teachers, families, and students as well as the artwork of students, I argue that the state’s ideals of citizenship are localized through place-based practices, which create informal learning spaces informed by class sentiments and ethnic sensibilities of teachers, students, and their families. These practices transform hegemonic narratives of citizenship, generating collaboration, creativity, sustainable lifestyle, and attachment to the place. I believe the focus on place-based education in the thesis can help teachers to design elective courses in curriculum based on localities, involving students and communities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".