Dynamics of Social Class, Race, and Place in Rural Education
Bibliographic record
Abstract
Half the world’s population lives in rural places, but education scholars and policy makers worldwide give little attention to rural of education. Indeed, most national systems, including in the developed world, treat their educational systems as institutions to 'modernize' the global economy.The authors in this volume have different concerns. They are rural education scholars from Australia, Canada, the United States, and Kyrgyzstan, and here their focus is the dynamics of social class: in particular rural schools but also in rural schooling as a local manifestation of a national (and the global) system.For the most part, the volume comprises relevant empirical reports, but none neglects theory, and some privilege theory and interpretation. First and last chapters introduce the texts and synthesize their joint and separate meanings. What are the implications of place for social class? How do class dynamics manifest differently in more and less racially homogeneous rural communities? How does place affect class and how might class affect place? How does schooling in rural communities reproduce or interrupt social-class mobility across generations? The chapters engage such questions more completely than other volumes in rural education, not as a final word or interm summary, but as an opening to an important line of inquiry thus far largely neglected in rural education scholarship.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".