Bibliographic record
Abstract
This dissertation takes a microhistorical approach to show how a late nineteenth- and early twentieth-century manual training school within Boston's North End-a predominantly Italian immigrant neighborhood-was a site of rhetorical education. This research is based upon qualitative coding and analysis of archival documents from the Records of the North Bennet Street Industrial School, 1880-1973 located at Schlesinger Library of the Radcliffe Institute for Advanced Study, Harvard University. I argue that the North Bennet Street Industrial School (NBSIS) and the Sloyd Training School for teachers that was housed within NBSIS, taught children of immigrants of elementary and grammar school age how to become industrious citizens through a manual training pedagogy, and specifically, the Sloyd method of handiwork. Each of my data chapters make separate interventions in rhetorical studies in the areas of embodied and student-centered pedagogies, citizenship production, and new materialism. While this research is in conversation with revisionist rhetorical histories, it moves beyond a discursive and linguistic view of rhetorical education while also disrupting generational lines that have traditionally existed within rhetorical education scholarship. I find that embodied, student-centered curriculum was employed to shape marginalized students, and in significant ways that cannot be discovered through studying education for adult learners or literacy practices alone. In addition to areas of rhetorical education and historiography, this research has implications for literacy studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".