Culturally Responsive Leadership: A Framework to Merge Eastern and Western Educational Philosophies in an Era of Increasing Globalization
Bibliographic record
Abstract
In the era of increasing globalization, education has become one of the media through which political, economic and cultural ideas are exchanged between nations and people. This paper looks at the dynamics and the epistemological challenges of delivering a Western (Canadian) curriculum, from the Deweyan lens, in an East Asian schooling context, namely China, that predominantly views education through the Confucian lens. I will employ a self-study research methodology to highlight some of these challenges, and demonstrate how the four pillars of the Culturally Responsive Leadership (CRL) framework can enhance cultural exchange and close the epistemological gap in a high school setting. This study underscores the importance of hiring and training culturally humble educators. Looking ahead, it will be beneficial to develop more strategies on ways teachers and leaders can unpack their biases when implementing the CRL framework; and develop an appreciation for other epistemological lenses through which one can narrow the gap between Eastern and Western educational philosophies. This way, educators can continue to promote cross-cultural understanding, which can potentially move us towards a more caring and inclusive world. Key words: globalization, epistemological gap, culturally responsive leadership, culturally humble educator, cross-cultural understanding, inclusive world
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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 | 0.020 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.067 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".