Exploring racism in Ontario’s public high schools: A case study research of Chinese students in Ontario and two public School Boards for a regional systems of innovation
Bibliographic record
Abstract
This paper aims to fill a knowledge gap regarding students of Chinese heritages’ experiences with racism and create a sample regional systems of innovation to show policymakers the possibility of change in public education. The paper examines the context within Ontario, Canada, and on a few occasions, borrows information from Vancouver, Canada; Saskatchewan, Canada; Scotland, United Kingdom; and the United States. Using Secondary Research methods, design empathy, and systemic inquiry to clarify what is quality and equitable schooling, as well as the assumptions that fundamentally and the current operating public high school education system in Ontario is still heavily under the colonial influences to expose possible errors, such as systemic racism and structural violence toward the Chinese, poor, and Minority. Last, this paper provides an intercultural, inclusive, and humanized solution, which is the sample regional systems of innovation based on System Boundary, Panarchy, and evaluated with Strategic Foresight, that can be change-making and liberating to all, including young white students, to increase quality and equity. This paper is an important study because education involves everyone and most likely is a stage of everyone’s life.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".