Higher Education Internationalization and English Language Instruction. Xiangying Huo. Springer, 2020
Bibliographic record
Abstract
The book "Higher Education Internationalization and English Language Instruction" is an autoethnographic work that examines the intersectionality of race and language in the Canadian higher education system. Through personal stories and narratives, the author explores themes such as native-speakerism, writing centre tutoring, multicultural education, and social justice. The book makes two significant contributions: first, it amplifies the voice of racialized individuals through the application of Critical Race Theory to personal experiences and diaries, serving as a springboard for thought and an invitation to dialogues on transformation. Second, it demonstrates the potential of personal narratives to reveal ideas that are often overlooked in positivist approaches, providing insight into methodological approaches that graduate students and young researchers can adopt. The book concludes with practical implications for addressing discriminatory systems and practises in universities to promote diversity and inclusiveness. The book follows a standard format for scholarly works and provides a useful background on the internationalisation of higher education and the significance of English as a medium for multiculturalism in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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".