Increasing Faculty Engagement: The Key to Meaningful and Sustainable Higher Education Internationalization
Bibliographic record
Abstract
According to the Association of Universities and Colleges of Canada (2014), over 80% of Canadian post-secondary institutions have identified internationalization as one of their top five priorities. However, the focus has been on inbound student mobility (King, 2018). Institutions have aggressively and successfully pursued student recruitment with international student populations increasing by approximately 78% from 2014/15 to 2019/20 (Statistics Canada, 2021). While rationalizing internationalization as a vehicle to improve academic and sociocultural outcomes, the literature suggests that universities are subjugating these objectives to economic and political motivations (de Wit, 2020; Garson, 2016). Strongly under the influence of neoliberal ideologies, post-secondary institutions focus their efforts on branding and other market-based initiatives to entice international students, while ignoring the investment required to engage faculty and develop quality internationalized curricula (Heringer, 2020; Nyangau, 2018). My organizational improvement plan (OIP) argues that faculty engagement is critical for meaningful and sustainable internationalization and recommends a comprehensive approach adapted from Childress’ (2008) Five I’s model of faculty engagement. The OIP is set in the context of a mid-size, primarily undergraduate university in British Columbia and is based on the principles of critical pedagogy as a foundation for quality learning (Freire, 2005; Giroux, 2013) and Bandura’s (1982) social cognitive theory as a mechanism to increase faculty engagement. The Competing Values Framework (Cameron & Quinn, 2011) is used to diagnose the gap between the current and desired state of internationalization. The OIP further outlines how a hybrid model of transactional/distributed leadership can be used to build faculty internationalization skills, improve self-efficacy, and increase engagement.
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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 | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".