Michael Dawson, Catherine Gidney, and Donald Wright, eds., Symbols of Canada
Bibliographic record
Abstract
Navigation School in Moscow by Aleksei A. Kurbatov in 1701, the Naval Academy in St. Petersburg by Baron Joseph de Saint-Hilaire in 1715, or the Noble Cadet Corps in St. Petersburg by Field Marshal B. C. von Münnich in 1731, in the wake of the constitutional crisis of 1730 that triggered clashes of ambition and factional battles.Once the schools were founded, these enterprisers used them as platforms and tools for self-assertion and self-promotion.Indeed, in all three cases, stakeholders of all stripes founded schools in order to further their agendas and ambitions, thus demonstrating that administrative entrepreneurship in education played a crucial role in eighteenth-century Russia.Interestingly, Fedyukin notes in his very first chapter that the Slavo-Greco-Latin Academy, established in Moscow by the brothers Leichoudes in 1685, "owed its existence to private funds" (39).Such a general conclusion is very much in line with a recent trend in the literature that minimizes the extent to which monarchies had fully developed centralized administrations in early modern Europe.In other words, state building was far from being complete -a reality that gave resourceful and inventive individuals the opportunity to launch educational projects.Scholars with a particular interest in institutional and organizational history will enjoy this well-written monograph, one that began its life as a doctoral dissertation at the University of North Carolina, Chapel Hill; those who do not share that enthusiasm may struggle to finish reading it.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".