Schoolteachers, Technologies of the Self, and the Genealogy of the Late Modern Subject: The <i>Bulletin</i> of the Queen’s Summer School Association, 1915–1932
Bibliographic record
Abstract
From the 1920s through the 1960s, thousands of Canadian schoolteachers spent much of their summer holiday period attending courses organized by universities or provincial departments of education. In the 1920s, Queen’s University attracted students from every Canadian province to attend summer school in Kingston, Ontario. Through a content analysis of one hundred messages written by schoolteachers from across Canada, this article analyzes a technology of the self deployed in an effort to encourage other schoolteachers to enrol at Queen’s University. The authors of these messages divided schoolteachers into two camps: those who were progressive, industrious, and ambitious and others. They positioned themselves and their readers as subjects with the responsibility of choosing the camp to which they belonged. This article decentres the study of power in the history of teacher education by analyzing how schoolteachers themselves governed schoolteachers. It demonstrates that technologies of the self are linked to the material interests of those who deploy them. The article furthers the genealogical study of late modern subjectivities by demonstrating that technologies of the self, which position human subjects as reflexive and autonomous beings responsible for shaping their own lives through conscious choices, were highly developed in the early twentieth century in Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".