Bibliographic record
Abstract
In the magical late 1960s, an amazing young scholar came, armed with a Harvard doctorate, to his first tenure-stream job at the Ontario Institute for Studies in Education (OISE), then in its second year as a new independent research and teaching centre affiliated with the University of Toronto. Our paths crossed; fortuitously, it was the summer of 1967, which coincided with the beginning of my Ph.D in U of T's history department. In one of those accidents that determine one's fate, my advisor Maurice Careless suggested that, since the focus of my research was to be the history of education, I should wander up to “that new place on Bloor Street” (OISE) to see about a course on the subject. There, the chair of the History & Philosophy of Education Department (H & P) steered me to Michael's new offering on the history of American education. Participation in this brilliant seminar was life changing. Embedded in intellectual, religious, cultural and social frameworks, and interpreting educational history to be more than the history of schools, his course led students to more questions than answers. I found both the course meetings and the readings riveting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".