Navigating managerial and entrepreneurial reforms in research‐intensive universities: A comparison of early career trajectories in Hong Kong and Canada
Bibliographic record
Abstract
Abstract This article conveys the results of a reflexive investigation of the managerial practices and entrepreneurial discourses that shape the academic trajectories of early career scholars. Beginning with the experiences of early career scholars in research‐intensive universities in Canada and Hong Kong, the authors explore some of the social and political‐economic relations that are reshaping higher education systems across the world. Drawing on experiences navigating university governance, funding and performance management processes, the authors explore how participation in the marketised relations of higher education inserts people into competition with colleagues within and beyond a single university context, instrumentalises and constrains relationships with civil sector collaborators, and produces a shared sense that nothing one does is ever enough. In this way, the article illuminates some of the ways a new global knowledge economy conditions academic life. 摘要 本文探討學術管理主義和企業論述如何塑造新晉學者的學術軌跡, 並匯報相關反思性研究的結果。本文以任職加拿大和香港研究型大學的早期新晉學者為例, 開始探討一些正在重塑全球高等教育體系的社會和政治經濟關係。作者利用新晉學者如何探索大學治理、資金和績效管理流程的經驗, 從而了解高等教育界的市場化關係如何將競爭意存植入在大學界的同事之間、令到與公民社會的協作關係工具化、並產生一種感到工作慣常不達標的意存。通過上述的方法, 本文闡明了新型全球知存經濟影響學術生活的一些方式。
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".