Presidential Address: Doctoral Theses and the Discipline of History in Canada, 1967 and 1985
Bibliographic record
Abstract
A comparison of doctoral theses in progress in 1967 and 1985 reveals a number of trends in historical studies in Canadian universities during the past two decades. In 1967, 58 per cent of all doctoral candidates chose topics in Canadian history and the largest number ― fully 36 per cent of all candidates ― were writing theses at the University of Toronto, which offered the broadest range of fields of any Canadian university. Much smaller programmes existed at McGill and the University of Western Ontario; aside from these three institutions, no other university in English-speaking Canada enrolled more than four students. Two-thirds of all francophone candidates were enrolled at Université Laval, where only five candidates were writing on topics other than Canadian history. The political process led the field of interest in all fields of study, while social history of the Annales school held little interest for either linguistic group. More than half the dissertations in Canadian fields were supervised by only eight senior scholars. By 1985, marked changes in this pattern were evident. The number of active doctoral candidates had increased from 236 in 1967 to 294, and Canadian history was the field of choice for 72 per cent. Doctoral programmes and hence supervision had decentralized in anglophone Canada, however, and the University of Toronto's dominance had been challenged by Queen's and York; specialized programmes of some size existed at a much larger number of institutions. Among francophone schools, enrollment had doubled and Laval had achieved a situation rivalling Toronto's in 1967. Laval and the Université de Montréal now had the largest doctoral programmes in the country. In terms of topic, policy and administration had replaced the political process as the subject of choice for both language groups; economic history experienced a modest degree of growth, while the history of ideas retained its traditional level of interest. Social history had become much more popular in both linguistic groups, while less European history was being studied. These developments pose both problems and possibilities for the profession as a whole. Doctoral studies have been enriched by the diversity of interests, but the potential for academic sectarian strife is troubling. The need now is for syntheses and paradigms which will permit the findings of subdisciplines to be integrated into a broader and more sensitive understanding of the past.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".