Funds,Faculty and Self-organization:The Experiences and Historical Investigation of Doctorate Cultivation in Canada
Bibliographic record
Abstract
The doctorate cultivation with relatively complete educational system has a history of 128 years in Canada.Along with the development of the doctorate cultivation,Canadian Federal government and provincial governments have progressively become dominant in financial outlay devotion.International talent flow has promoted the diversification of doctoral supervisor teams in Canada.The highly self-organized Academic Organizations in Canadian universities keep the balance of the internal and external forces.According to the current circumstances in China,we may draw lessons from the following three aspects.Firstly,as the departments in charge of education,governments at all levels should continuously increase the educational funds and money for research.Secondly,as the development platform of teachers,universities should pay close attention to the high quality and internationalization of doctoral supervisor teams,and estabish the evaduation system of development achievements.Thirdly,as the micro circumstances of doctorate cultivation,Academic Organizations should contribute more in control the size of academic degrees and the survival of the fittest.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.053 | 0.017 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".