Border within Borders: Ontario’s Canadian and World Studies Curriculum
Bibliographic record
Abstract
Canada is a country comprised of many people, many identities, many cultures, and thus, many borders. These borders, whether demarcated with provincial, municipal or imaginary lines, play a large role in influencing the current position and efficacy of our education system., Having gone through major changes in the last ten years, the Ontario curriculum is an example of how borders influence both what is taught and what is excluded. By focusing on current theories of border politics in Canada and two courses in the Canadian and World Studies section of the Ontario Curriculum – Canadian History Since World War I, Grade 10, Academic; and Canada: History, Identity, and Culture, Grade 12, University Preparation – I argue that current curriculum and political theories undermine the official Canadian policy of multiculturalism. A discussion on current political border theory as it applies to Canada will outline the main arguments for borders and their place in Canadian policy. A look at current educational thinking will outline the current role of education in society, and an examination of the Ontario Curriculum documents will shed light on the idea that the institution of Borders within Borders TAYLOR
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".