Bibliographic record
Abstract
随着社会经济的迅速发展和国际交流的日益频繁，高等教育的国际化和多样化成为许多国家高等教育发展的重要特征。加拿大作为一个移民国家，由于地域广阔，高等教育也因此具有明显的多样性和地方差异性，同时作为高等教育的发达国家。加拿大高等教育在国际化方面也走在比较前沿的位置。了解加拿大高等教育发展的经验和措施，对同样是高等教育大国和高等教育正在逐步走向世界的中国将有很多有益的借鉴。本期我们有幸采访了加拿大多伦多大学的格兰·琼斯教授，期望能在更全面地了解加拿大特有的政治、经济、文化背景下深入理解其高等教育。 格兰·琼斯（Glen A．Jones）教授是教育学博士，现为加拿大多伦多大学安大略教育研究院（OISE／UT）的科研副院长，迄今已发表40多篇有关加拿大高等教育体制的论文，是新版《世界教育百科全书》（加拿大分册）的撰稿人、加拿大高等教育学会前任主席以及《加拿大高等教育》杂志的副主编。琼斯教授已出版的书籍包括：1997年出版的《加拿大高等教育：不同体系与不同视角》（Higher Education in Canada：Different Systems，Different Perspectives）、1998年出版的《大学与国家：对加拿大经验的反思》（The University and the State：Reflections on the Canadian Experience）、2002年与Alberto Amaral、Befit Karseth合作出版的《高等教育机构管理的国家视角》（Governing Higher Education：National Perspectiveson Institutional Governance）、2005年与Patricia McCarney和Michael Skolnik合作出版的《创造知识，巩固国家：高等教育角色的变化》（Creating Knowledge，Strengthening Nations：The Changing Role of Higher Education）等等。2001年，加拿大高等教育研究协会授予琼斯教授事业研究奖。琼斯教授的研究和教学领域主要集中在高等教育制度、政策和管理，他�
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".