Higher Education Leadership and the Internationalization Imaginary: Where Personal Biography Meets the Socio-Historical
Bibliographic record
Abstract
In this chapter, we explore how higher education institution (HEI) leaders perceive the relationship between their international background and their commitment to and vision for internationalization. Our ten Canadian HEI participants thought there was a direct link between their international backgrounds and commitment to internationalization. While all spoke of the benefits of internationalization, some viewed internationalization through an ethical, socio-cultural lens whereas others privileged internationalization’s instrumental values. We point to tensions facing some leaders in reconciling their ideal visions of internationalization with neoliberal pressures facing HEIs in a global era. We demonstrate the importance of attending to the inter-relationships between broader socio-historical drivers of internationalization and the personal biographies of those charged with advancing internationalization agendas. Our findings lead us to develop a new theoretical concept, which we term the “internationalization imaginary”, to understand the interplay between the individual, local, national and global forces shaping internationalization in higher education. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".